Package 'paws.machine.learning'

Title: 'Amazon Web Services' Machine Learning Services
Description: Interface to 'Amazon Web Services' machine learning services, including 'SageMaker' managed machine learning service, natural language processing, speech recognition, translation, and more <https://aws.amazon.com/machine-learning/>.
Authors: David Kretch [aut], Adam Banker [aut], Dyfan Jones [cre], Amazon.com, Inc. [cph]
Maintainer: Dyfan Jones <[email protected]>
License: Apache License (>= 2.0)
Version: 0.7.0
Built: 2024-11-03 05:53:16 UTC
Source: https://github.com/paws-r/paws

Help Index


Amazon Augmented AI Runtime

Description

Amazon Augmented AI (Amazon A2I) adds the benefit of human judgment to any machine learning application. When an AI application can't evaluate data with a high degree of confidence, human reviewers can take over. This human review is called a human review workflow. To create and start a human review workflow, you need three resources: a worker task template, a flow definition, and a human loop.

For information about these resources and prerequisites for using Amazon A2I, see Get Started with Amazon Augmented AI in the Amazon SageMaker Developer Guide.

This API reference includes information about API actions and data types that you can use to interact with Amazon A2I programmatically. Use this guide to:

  • Start a human loop with the start_human_loop operation when using Amazon A2I with a custom task type. To learn more about the difference between custom and built-in task types, see Use Task Types . To learn how to start a human loop using this API, see Create and Start a Human Loop for a Custom Task Type in the Amazon SageMaker Developer Guide.

  • Manage your human loops. You can list all human loops that you have created, describe individual human loops, and stop and delete human loops. To learn more, see Monitor and Manage Your Human Loop in the Amazon SageMaker Developer Guide.

Amazon A2I integrates APIs from various AWS services to create and start human review workflows for those services. To learn how Amazon A2I uses these APIs, see Use APIs in Amazon A2I in the Amazon SageMaker Developer Guide.

Usage

augmentedairuntime(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- augmentedairuntime(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

delete_human_loop Deletes the specified human loop for a flow definition
describe_human_loop Returns information about the specified human loop
list_human_loops Returns information about human loops, given the specified parameters
start_human_loop Starts a human loop, provided that at least one activation condition is met
stop_human_loop Stops the specified human loop

Examples

## Not run: 
svc <- augmentedairuntime()
svc$delete_human_loop(
  Foo = 123
)

## End(Not run)

Amazon Bedrock

Description

Describes the API operations for creating, managing, fine-turning, and evaluating Amazon Bedrock models.

Usage

bedrock(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- bedrock(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

batch_delete_evaluation_job Creates a batch deletion job
create_evaluation_job API operation for creating and managing Amazon Bedrock automatic model evaluation jobs and model evaluation jobs that use human workers
create_guardrail Creates a guardrail to block topics and to implement safeguards for your generative AI applications
create_guardrail_version Creates a version of the guardrail
create_model_copy_job Copies a model to another region so that it can be used there
create_model_customization_job Creates a fine-tuning job to customize a base model
create_model_import_job Creates a model import job to import model that you have customized in other environments, such as Amazon SageMaker
create_model_invocation_job Creates a batch inference job to invoke a model on multiple prompts
create_provisioned_model_throughput Creates dedicated throughput for a base or custom model with the model units and for the duration that you specify
delete_custom_model Deletes a custom model that you created earlier
delete_guardrail Deletes a guardrail
delete_imported_model Deletes a custom model that you imported earlier
delete_model_invocation_logging_configuration Delete the invocation logging
delete_provisioned_model_throughput Deletes a Provisioned Throughput
get_custom_model Get the properties associated with a Amazon Bedrock custom model that you have created
get_evaluation_job Retrieves the properties associated with a model evaluation job, including the status of the job
get_foundation_model Get details about a Amazon Bedrock foundation model
get_guardrail Gets details about a guardrail
get_imported_model Gets properties associated with a customized model you imported
get_inference_profile Gets information about an inference profile
get_model_copy_job Retrieves information about a model copy job
get_model_customization_job Retrieves the properties associated with a model-customization job, including the status of the job
get_model_import_job Retrieves the properties associated with import model job, including the status of the job
get_model_invocation_job Gets details about a batch inference job
get_model_invocation_logging_configuration Get the current configuration values for model invocation logging
get_provisioned_model_throughput Returns details for a Provisioned Throughput
list_custom_models Returns a list of the custom models that you have created with the CreateModelCustomizationJob operation
list_evaluation_jobs Lists model evaluation jobs
list_foundation_models Lists Amazon Bedrock foundation models that you can use
list_guardrails Lists details about all the guardrails in an account
list_imported_models Returns a list of models you've imported
list_inference_profiles Returns a list of inference profiles that you can use
list_model_copy_jobs Returns a list of model copy jobs that you have submitted
list_model_customization_jobs Returns a list of model customization jobs that you have submitted
list_model_import_jobs Returns a list of import jobs you've submitted
list_model_invocation_jobs Lists all batch inference jobs in the account
list_provisioned_model_throughputs Lists the Provisioned Throughputs in the account
list_tags_for_resource List the tags associated with the specified resource
put_model_invocation_logging_configuration Set the configuration values for model invocation logging
stop_evaluation_job Stops an in progress model evaluation job
stop_model_customization_job Stops an active model customization job
stop_model_invocation_job Stops a batch inference job
tag_resource Associate tags with a resource
untag_resource Remove one or more tags from a resource
update_guardrail Updates a guardrail with the values you specify
update_provisioned_model_throughput Updates the name or associated model for a Provisioned Throughput

Examples

## Not run: 
svc <- bedrock()
svc$batch_delete_evaluation_job(
  Foo = 123
)

## End(Not run)

Amazon Bedrock Runtime

Description

Describes the API operations for running inference using Amazon Bedrock models.

Usage

bedrockruntime(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- bedrockruntime(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

apply_guardrail The action to apply a guardrail
converse Sends messages to the specified Amazon Bedrock model
converse_stream Sends messages to the specified Amazon Bedrock model and returns the response in a stream
invoke_model Invokes the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body
invoke_model_with_response_stream Invoke the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body

Examples

## Not run: 
svc <- bedrockruntime()
svc$apply_guardrail(
  Foo = 123
)

## End(Not run)

Amazon Comprehend

Description

Amazon Comprehend is an Amazon Web Services service for gaining insight into the content of documents. Use these actions to determine the topics contained in your documents, the topics they discuss, the predominant sentiment expressed in them, the predominant language used, and more.

Usage

comprehend(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- comprehend(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

batch_detect_dominant_language Determines the dominant language of the input text for a batch of documents
batch_detect_entities Inspects the text of a batch of documents for named entities and returns information about them
batch_detect_key_phrases Detects the key noun phrases found in a batch of documents
batch_detect_sentiment Inspects a batch of documents and returns an inference of the prevailing sentiment, POSITIVE, NEUTRAL, MIXED, or NEGATIVE, in each one
batch_detect_syntax Inspects the text of a batch of documents for the syntax and part of speech of the words in the document and returns information about them
batch_detect_targeted_sentiment Inspects a batch of documents and returns a sentiment analysis for each entity identified in the documents
classify_document Creates a classification request to analyze a single document in real-time
contains_pii_entities Analyzes input text for the presence of personally identifiable information (PII) and returns the labels of identified PII entity types such as name, address, bank account number, or phone number
create_dataset Creates a dataset to upload training or test data for a model associated with a flywheel
create_document_classifier Creates a new document classifier that you can use to categorize documents
create_endpoint Creates a model-specific endpoint for synchronous inference for a previously trained custom model For information about endpoints, see Managing endpoints
create_entity_recognizer Creates an entity recognizer using submitted files
create_flywheel A flywheel is an Amazon Web Services resource that orchestrates the ongoing training of a model for custom classification or custom entity recognition
delete_document_classifier Deletes a previously created document classifier
delete_endpoint Deletes a model-specific endpoint for a previously-trained custom model
delete_entity_recognizer Deletes an entity recognizer
delete_flywheel Deletes a flywheel
delete_resource_policy Deletes a resource-based policy that is attached to a custom model
describe_dataset Returns information about the dataset that you specify
describe_document_classification_job Gets the properties associated with a document classification job
describe_document_classifier Gets the properties associated with a document classifier
describe_dominant_language_detection_job Gets the properties associated with a dominant language detection job
describe_endpoint Gets the properties associated with a specific endpoint
describe_entities_detection_job Gets the properties associated with an entities detection job
describe_entity_recognizer Provides details about an entity recognizer including status, S3 buckets containing training data, recognizer metadata, metrics, and so on
describe_events_detection_job Gets the status and details of an events detection job
describe_flywheel Provides configuration information about the flywheel
describe_flywheel_iteration Retrieve the configuration properties of a flywheel iteration
describe_key_phrases_detection_job Gets the properties associated with a key phrases detection job
describe_pii_entities_detection_job Gets the properties associated with a PII entities detection job
describe_resource_policy Gets the details of a resource-based policy that is attached to a custom model, including the JSON body of the policy
describe_sentiment_detection_job Gets the properties associated with a sentiment detection job
describe_targeted_sentiment_detection_job Gets the properties associated with a targeted sentiment detection job
describe_topics_detection_job Gets the properties associated with a topic detection job
detect_dominant_language Determines the dominant language of the input text
detect_entities Detects named entities in input text when you use the pre-trained model
detect_key_phrases Detects the key noun phrases found in the text
detect_pii_entities Inspects the input text for entities that contain personally identifiable information (PII) and returns information about them
detect_sentiment Inspects text and returns an inference of the prevailing sentiment (POSITIVE, NEUTRAL, MIXED, or NEGATIVE)
detect_syntax Inspects text for syntax and the part of speech of words in the document
detect_targeted_sentiment Inspects the input text and returns a sentiment analysis for each entity identified in the text
detect_toxic_content Performs toxicity analysis on the list of text strings that you provide as input
import_model Creates a new custom model that replicates a source custom model that you import
list_datasets List the datasets that you have configured in this Region
list_document_classification_jobs Gets a list of the documentation classification jobs that you have submitted
list_document_classifiers Gets a list of the document classifiers that you have created
list_document_classifier_summaries Gets a list of summaries of the document classifiers that you have created
list_dominant_language_detection_jobs Gets a list of the dominant language detection jobs that you have submitted
list_endpoints Gets a list of all existing endpoints that you've created
list_entities_detection_jobs Gets a list of the entity detection jobs that you have submitted
list_entity_recognizers Gets a list of the properties of all entity recognizers that you created, including recognizers currently in training
list_entity_recognizer_summaries Gets a list of summaries for the entity recognizers that you have created
list_events_detection_jobs Gets a list of the events detection jobs that you have submitted
list_flywheel_iteration_history Information about the history of a flywheel iteration
list_flywheels Gets a list of the flywheels that you have created
list_key_phrases_detection_jobs Get a list of key phrase detection jobs that you have submitted
list_pii_entities_detection_jobs Gets a list of the PII entity detection jobs that you have submitted
list_sentiment_detection_jobs Gets a list of sentiment detection jobs that you have submitted
list_tags_for_resource Lists all tags associated with a given Amazon Comprehend resource
list_targeted_sentiment_detection_jobs Gets a list of targeted sentiment detection jobs that you have submitted
list_topics_detection_jobs Gets a list of the topic detection jobs that you have submitted
put_resource_policy Attaches a resource-based policy to a custom model
start_document_classification_job Starts an asynchronous document classification job using a custom classification model
start_dominant_language_detection_job Starts an asynchronous dominant language detection job for a collection of documents
start_entities_detection_job Starts an asynchronous entity detection job for a collection of documents
start_events_detection_job Starts an asynchronous event detection job for a collection of documents
start_flywheel_iteration Start the flywheel iteration
start_key_phrases_detection_job Starts an asynchronous key phrase detection job for a collection of documents
start_pii_entities_detection_job Starts an asynchronous PII entity detection job for a collection of documents
start_sentiment_detection_job Starts an asynchronous sentiment detection job for a collection of documents
start_targeted_sentiment_detection_job Starts an asynchronous targeted sentiment detection job for a collection of documents
start_topics_detection_job Starts an asynchronous topic detection job
stop_dominant_language_detection_job Stops a dominant language detection job in progress
stop_entities_detection_job Stops an entities detection job in progress
stop_events_detection_job Stops an events detection job in progress
stop_key_phrases_detection_job Stops a key phrases detection job in progress
stop_pii_entities_detection_job Stops a PII entities detection job in progress
stop_sentiment_detection_job Stops a sentiment detection job in progress
stop_targeted_sentiment_detection_job Stops a targeted sentiment detection job in progress
stop_training_document_classifier Stops a document classifier training job while in progress
stop_training_entity_recognizer Stops an entity recognizer training job while in progress
tag_resource Associates a specific tag with an Amazon Comprehend resource
untag_resource Removes a specific tag associated with an Amazon Comprehend resource
update_endpoint Updates information about the specified endpoint
update_flywheel Update the configuration information for an existing flywheel

Examples

## Not run: 
svc <- comprehend()
svc$batch_detect_dominant_language(
  Foo = 123
)

## End(Not run)

AWS Comprehend Medical

Description

Amazon Comprehend Medical extracts structured information from unstructured clinical text. Use these actions to gain insight in your documents. Amazon Comprehend Medical only detects entities in English language texts. Amazon Comprehend Medical places limits on the sizes of files allowed for different API operations. To learn more, see Guidelines and quotas in the Amazon Comprehend Medical Developer Guide.

Usage

comprehendmedical(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- comprehendmedical(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

describe_entities_detection_v2_job Gets the properties associated with a medical entities detection job
describe_icd10cm_inference_job Gets the properties associated with an InferICD10CM job
describe_phi_detection_job Gets the properties associated with a protected health information (PHI) detection job
describe_rx_norm_inference_job Gets the properties associated with an InferRxNorm job
describe_snomedct_inference_job Gets the properties associated with an InferSNOMEDCT job
detect_entities The DetectEntities operation is deprecated
detect_entities_v2 Inspects the clinical text for a variety of medical entities and returns specific information about them such as entity category, location, and confidence score on that information
detect_phi Inspects the clinical text for protected health information (PHI) entities and returns the entity category, location, and confidence score for each entity
infer_icd10cm InferICD10CM detects medical conditions as entities listed in a patient record and links those entities to normalized concept identifiers in the ICD-10-CM knowledge base from the Centers for Disease Control
infer_rx_norm InferRxNorm detects medications as entities listed in a patient record and links to the normalized concept identifiers in the RxNorm database from the National Library of Medicine
infer_snomedct InferSNOMEDCT detects possible medical concepts as entities and links them to codes from the Systematized Nomenclature of Medicine, Clinical Terms (SNOMED-CT) ontology
list_entities_detection_v2_jobs Gets a list of medical entity detection jobs that you have submitted
list_icd10cm_inference_jobs Gets a list of InferICD10CM jobs that you have submitted
list_phi_detection_jobs Gets a list of protected health information (PHI) detection jobs you have submitted
list_rx_norm_inference_jobs Gets a list of InferRxNorm jobs that you have submitted
list_snomedct_inference_jobs Gets a list of InferSNOMEDCT jobs a user has submitted
start_entities_detection_v2_job Starts an asynchronous medical entity detection job for a collection of documents
start_icd10cm_inference_job Starts an asynchronous job to detect medical conditions and link them to the ICD-10-CM ontology
start_phi_detection_job Starts an asynchronous job to detect protected health information (PHI)
start_rx_norm_inference_job Starts an asynchronous job to detect medication entities and link them to the RxNorm ontology
start_snomedct_inference_job Starts an asynchronous job to detect medical concepts and link them to the SNOMED-CT ontology
stop_entities_detection_v2_job Stops a medical entities detection job in progress
stop_icd10cm_inference_job Stops an InferICD10CM inference job in progress
stop_phi_detection_job Stops a protected health information (PHI) detection job in progress
stop_rx_norm_inference_job Stops an InferRxNorm inference job in progress
stop_snomedct_inference_job Stops an InferSNOMEDCT inference job in progress

Examples

## Not run: 
svc <- comprehendmedical()
svc$describe_entities_detection_v2_job(
  Foo = 123
)

## End(Not run)

Amazon Elastic Inference

Description

Elastic Inference public APIs.

February 15, 2023: Starting April 15, 2023, AWS will not onboard new customers to Amazon Elastic Inference (EI), and will help current customers migrate their workloads to options that offer better price and performance. After April 15, 2023, new customers will not be able to launch instances with Amazon EI accelerators in Amazon SageMaker, Amazon ECS, or Amazon EC2. However, customers who have used Amazon EI at least once during the past 30-day period are considered current customers and will be able to continue using the service.

Usage

elasticinference(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- elasticinference(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

describe_accelerator_offerings Describes the locations in which a given accelerator type or set of types is present in a given region
describe_accelerators Describes information over a provided set of accelerators belonging to an account
describe_accelerator_types Describes the accelerator types available in a given region, as well as their characteristics, such as memory and throughput
list_tags_for_resource Returns all tags of an Elastic Inference Accelerator
tag_resource Adds the specified tags to an Elastic Inference Accelerator
untag_resource Removes the specified tags from an Elastic Inference Accelerator

Examples

## Not run: 
svc <- elasticinference()
svc$describe_accelerator_offerings(
  Foo = 123
)

## End(Not run)

Amazon Forecast Query Service

Description

Provides APIs for creating and managing Amazon Forecast resources.

Usage

forecastqueryservice(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- forecastqueryservice(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

query_forecast Retrieves a forecast for a single item, filtered by the supplied criteria
query_what_if_forecast Retrieves a what-if forecast

Examples

## Not run: 
svc <- forecastqueryservice()
svc$query_forecast(
  Foo = 123
)

## End(Not run)

Amazon Forecast Service

Description

Provides APIs for creating and managing Amazon Forecast resources.

Usage

forecastservice(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- forecastservice(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

create_auto_predictor Creates an Amazon Forecast predictor
create_dataset Creates an Amazon Forecast dataset
create_dataset_group Creates a dataset group, which holds a collection of related datasets
create_dataset_import_job Imports your training data to an Amazon Forecast dataset
create_explainability Explainability is only available for Forecasts and Predictors generated from an AutoPredictor (CreateAutoPredictor)
create_explainability_export Exports an Explainability resource created by the CreateExplainability operation
create_forecast Creates a forecast for each item in the TARGET_TIME_SERIES dataset that was used to train the predictor
create_forecast_export_job Exports a forecast created by the CreateForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket
create_monitor Creates a predictor monitor resource for an existing auto predictor
create_predictor This operation creates a legacy predictor that does not include all the predictor functionalities provided by Amazon Forecast
create_predictor_backtest_export_job Exports backtest forecasts and accuracy metrics generated by the CreateAutoPredictor or CreatePredictor operations
create_what_if_analysis What-if analysis is a scenario modeling technique where you make a hypothetical change to a time series and compare the forecasts generated by these changes against the baseline, unchanged time series
create_what_if_forecast A what-if forecast is a forecast that is created from a modified version of the baseline forecast
create_what_if_forecast_export Exports a forecast created by the CreateWhatIfForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket
delete_dataset Deletes an Amazon Forecast dataset that was created using the CreateDataset operation
delete_dataset_group Deletes a dataset group created using the CreateDatasetGroup operation
delete_dataset_import_job Deletes a dataset import job created using the CreateDatasetImportJob operation
delete_explainability Deletes an Explainability resource
delete_explainability_export Deletes an Explainability export
delete_forecast Deletes a forecast created using the CreateForecast operation
delete_forecast_export_job Deletes a forecast export job created using the CreateForecastExportJob operation
delete_monitor Deletes a monitor resource
delete_predictor Deletes a predictor created using the DescribePredictor or CreatePredictor operations
delete_predictor_backtest_export_job Deletes a predictor backtest export job
delete_resource_tree Deletes an entire resource tree
delete_what_if_analysis Deletes a what-if analysis created using the CreateWhatIfAnalysis operation
delete_what_if_forecast Deletes a what-if forecast created using the CreateWhatIfForecast operation
delete_what_if_forecast_export Deletes a what-if forecast export created using the CreateWhatIfForecastExport operation
describe_auto_predictor Describes a predictor created using the CreateAutoPredictor operation
describe_dataset Describes an Amazon Forecast dataset created using the CreateDataset operation
describe_dataset_group Describes a dataset group created using the CreateDatasetGroup operation
describe_dataset_import_job Describes a dataset import job created using the CreateDatasetImportJob operation
describe_explainability Describes an Explainability resource created using the CreateExplainability operation
describe_explainability_export Describes an Explainability export created using the CreateExplainabilityExport operation
describe_forecast Describes a forecast created using the CreateForecast operation
describe_forecast_export_job Describes a forecast export job created using the CreateForecastExportJob operation
describe_monitor Describes a monitor resource
describe_predictor This operation is only valid for legacy predictors created with CreatePredictor
describe_predictor_backtest_export_job Describes a predictor backtest export job created using the CreatePredictorBacktestExportJob operation
describe_what_if_analysis Describes the what-if analysis created using the CreateWhatIfAnalysis operation
describe_what_if_forecast Describes the what-if forecast created using the CreateWhatIfForecast operation
describe_what_if_forecast_export Describes the what-if forecast export created using the CreateWhatIfForecastExport operation
get_accuracy_metrics Provides metrics on the accuracy of the models that were trained by the CreatePredictor operation
list_dataset_groups Returns a list of dataset groups created using the CreateDatasetGroup operation
list_dataset_import_jobs Returns a list of dataset import jobs created using the CreateDatasetImportJob operation
list_datasets Returns a list of datasets created using the CreateDataset operation
list_explainabilities Returns a list of Explainability resources created using the CreateExplainability operation
list_explainability_exports Returns a list of Explainability exports created using the CreateExplainabilityExport operation
list_forecast_export_jobs Returns a list of forecast export jobs created using the CreateForecastExportJob operation
list_forecasts Returns a list of forecasts created using the CreateForecast operation
list_monitor_evaluations Returns a list of the monitoring evaluation results and predictor events collected by the monitor resource during different windows of time
list_monitors Returns a list of monitors created with the CreateMonitor operation and CreateAutoPredictor operation
list_predictor_backtest_export_jobs Returns a list of predictor backtest export jobs created using the CreatePredictorBacktestExportJob operation
list_predictors Returns a list of predictors created using the CreateAutoPredictor or CreatePredictor operations
list_tags_for_resource Lists the tags for an Amazon Forecast resource
list_what_if_analyses Returns a list of what-if analyses created using the CreateWhatIfAnalysis operation
list_what_if_forecast_exports Returns a list of what-if forecast exports created using the CreateWhatIfForecastExport operation
list_what_if_forecasts Returns a list of what-if forecasts created using the CreateWhatIfForecast operation
resume_resource Resumes a stopped monitor resource
stop_resource Stops a resource
tag_resource Associates the specified tags to a resource with the specified resourceArn
untag_resource Deletes the specified tags from a resource
update_dataset_group Replaces the datasets in a dataset group with the specified datasets

Examples

## Not run: 
svc <- forecastservice()
svc$create_auto_predictor(
  Foo = 123
)

## End(Not run)

Amazon Fraud Detector

Description

This is the Amazon Fraud Detector API Reference. This guide is for developers who need detailed information about Amazon Fraud Detector API actions, data types, and errors. For more information about Amazon Fraud Detector features, see the Amazon Fraud Detector User Guide.

We provide the Query API as well as AWS software development kits (SDK) for Amazon Fraud Detector in Java and Python programming languages.

The Amazon Fraud Detector Query API provides HTTPS requests that use the HTTP verb GET or POST and a Query parameter Action. AWS SDK provides libraries, sample code, tutorials, and other resources for software developers who prefer to build applications using language-specific APIs instead of submitting a request over HTTP or HTTPS. These libraries provide basic functions that automatically take care of tasks such as cryptographically signing your requests, retrying requests, and handling error responses, so that it is easier for you to get started. For more information about the AWS SDKs, go to Tools to build on AWS page, scroll down to the SDK section, and choose plus (+) sign to expand the section.

Usage

frauddetector(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- frauddetector(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

batch_create_variable Creates a batch of variables
batch_get_variable Gets a batch of variables
cancel_batch_import_job Cancels an in-progress batch import job
cancel_batch_prediction_job Cancels the specified batch prediction job
create_batch_import_job Creates a batch import job
create_batch_prediction_job Creates a batch prediction job
create_detector_version Creates a detector version
create_list Creates a list
create_model Creates a model using the specified model type
create_model_version Creates a version of the model using the specified model type and model id
create_rule Creates a rule for use with the specified detector
create_variable Creates a variable
delete_batch_import_job Deletes the specified batch import job ID record
delete_batch_prediction_job Deletes a batch prediction job
delete_detector Deletes the detector
delete_detector_version Deletes the detector version
delete_entity_type Deletes an entity type
delete_event Deletes the specified event
delete_events_by_event_type Deletes all events of a particular event type
delete_event_type Deletes an event type
delete_external_model Removes a SageMaker model from Amazon Fraud Detector
delete_label Deletes a label
delete_list Deletes the list, provided it is not used in a rule
delete_model Deletes a model
delete_model_version Deletes a model version
delete_outcome Deletes an outcome
delete_rule Deletes the rule
delete_variable Deletes a variable
describe_detector Gets all versions for a specified detector
describe_model_versions Gets all of the model versions for the specified model type or for the specified model type and model ID
get_batch_import_jobs Gets all batch import jobs or a specific job of the specified ID
get_batch_prediction_jobs Gets all batch prediction jobs or a specific job if you specify a job ID
get_delete_events_by_event_type_status Retrieves the status of a DeleteEventsByEventType action
get_detectors Gets all detectors or a single detector if a detectorId is specified
get_detector_version Gets a particular detector version
get_entity_types Gets all entity types or a specific entity type if a name is specified
get_event Retrieves details of events stored with Amazon Fraud Detector
get_event_prediction Evaluates an event against a detector version
get_event_prediction_metadata Gets details of the past fraud predictions for the specified event ID, event type, detector ID, and detector version ID that was generated in the specified time period
get_event_types Gets all event types or a specific event type if name is provided
get_external_models Gets the details for one or more Amazon SageMaker models that have been imported into the service
get_kms_encryption_key Gets the encryption key if a KMS key has been specified to be used to encrypt content in Amazon Fraud Detector
get_labels Gets all labels or a specific label if name is provided
get_list_elements Gets all the elements in the specified list
get_lists_metadata Gets the metadata of either all the lists under the account or the specified list
get_models Gets one or more models
get_model_version Gets the details of the specified model version
get_outcomes Gets one or more outcomes
get_rules Get all rules for a detector (paginated) if ruleId and ruleVersion are not specified
get_variables Gets all of the variables or the specific variable
list_event_predictions Gets a list of past predictions
list_tags_for_resource Lists all tags associated with the resource
put_detector Creates or updates a detector
put_entity_type Creates or updates an entity type
put_event_type Creates or updates an event type
put_external_model Creates or updates an Amazon SageMaker model endpoint
put_kms_encryption_key Specifies the KMS key to be used to encrypt content in Amazon Fraud Detector
put_label Creates or updates label
put_outcome Creates or updates an outcome
send_event Stores events in Amazon Fraud Detector without generating fraud predictions for those events
tag_resource Assigns tags to a resource
untag_resource Removes tags from a resource
update_detector_version Updates a detector version
update_detector_version_metadata Updates the detector version's description
update_detector_version_status Updates the detector version’s status
update_event_label Updates the specified event with a new label
update_list Updates a list
update_model Updates model description
update_model_version Updates a model version
update_model_version_status Updates the status of a model version
update_rule_metadata Updates a rule's metadata
update_rule_version Updates a rule version resulting in a new rule version
update_variable Updates a variable

Examples

## Not run: 
svc <- frauddetector()
svc$batch_create_variable(
  Foo = 123
)

## End(Not run)

Amazon Lex Model Building Service

Description

Amazon Lex Build-Time Actions

Amazon Lex is an AWS service for building conversational voice and text interfaces. Use these actions to create, update, and delete conversational bots for new and existing client applications.

Usage

lexmodelbuildingservice(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- lexmodelbuildingservice(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

create_bot_version Creates a new version of the bot based on the $LATEST version
create_intent_version Creates a new version of an intent based on the $LATEST version of the intent
create_slot_type_version Creates a new version of a slot type based on the $LATEST version of the specified slot type
delete_bot Deletes all versions of the bot, including the $LATEST version
delete_bot_alias Deletes an alias for the specified bot
delete_bot_channel_association Deletes the association between an Amazon Lex bot and a messaging platform
delete_bot_version Deletes a specific version of a bot
delete_intent Deletes all versions of the intent, including the $LATEST version
delete_intent_version Deletes a specific version of an intent
delete_slot_type Deletes all versions of the slot type, including the $LATEST version
delete_slot_type_version Deletes a specific version of a slot type
delete_utterances Deletes stored utterances
get_bot Returns metadata information for a specific bot
get_bot_alias Returns information about an Amazon Lex bot alias
get_bot_aliases Returns a list of aliases for a specified Amazon Lex bot
get_bot_channel_association Returns information about the association between an Amazon Lex bot and a messaging platform
get_bot_channel_associations Returns a list of all of the channels associated with the specified bot
get_bots Returns bot information as follows:
get_bot_versions Gets information about all of the versions of a bot
get_builtin_intent Returns information about a built-in intent
get_builtin_intents Gets a list of built-in intents that meet the specified criteria
get_builtin_slot_types Gets a list of built-in slot types that meet the specified criteria
get_export Exports the contents of a Amazon Lex resource in a specified format
get_import Gets information about an import job started with the StartImport operation
get_intent Returns information about an intent
get_intents Returns intent information as follows:
get_intent_versions Gets information about all of the versions of an intent
get_migration Provides details about an ongoing or complete migration from an Amazon Lex V1 bot to an Amazon Lex V2 bot
get_migrations Gets a list of migrations between Amazon Lex V1 and Amazon Lex V2
get_slot_type Returns information about a specific version of a slot type
get_slot_types Returns slot type information as follows:
get_slot_type_versions Gets information about all versions of a slot type
get_utterances_view Use the GetUtterancesView operation to get information about the utterances that your users have made to your bot
list_tags_for_resource Gets a list of tags associated with the specified resource
put_bot Creates an Amazon Lex conversational bot or replaces an existing bot
put_bot_alias Creates an alias for the specified version of the bot or replaces an alias for the specified bot
put_intent Creates an intent or replaces an existing intent
put_slot_type Creates a custom slot type or replaces an existing custom slot type
start_import Starts a job to import a resource to Amazon Lex
start_migration Starts migrating a bot from Amazon Lex V1 to Amazon Lex V2
tag_resource Adds the specified tags to the specified resource
untag_resource Removes tags from a bot, bot alias or bot channel

Examples

## Not run: 
svc <- lexmodelbuildingservice()
# This example shows how to get configuration information for a bot.
svc$get_bot(
  name = "DocOrderPizza",
  versionOrAlias = "$LATEST"
)

## End(Not run)

Amazon Lex Model Building V2

Description

Amazon Lex Model Building V2

Usage

lexmodelsv2(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- lexmodelsv2(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

batch_create_custom_vocabulary_item Create a batch of custom vocabulary items for a given bot locale's custom vocabulary
batch_delete_custom_vocabulary_item Delete a batch of custom vocabulary items for a given bot locale's custom vocabulary
batch_update_custom_vocabulary_item Update a batch of custom vocabulary items for a given bot locale's custom vocabulary
build_bot_locale Builds a bot, its intents, and its slot types into a specific locale
create_bot Creates an Amazon Lex conversational bot
create_bot_alias Creates an alias for the specified version of a bot
create_bot_locale Creates a locale in the bot
create_bot_replica Action to create a replication of the source bot in the secondary region
create_bot_version Creates an immutable version of the bot
create_export Creates a zip archive containing the contents of a bot or a bot locale
create_intent Creates an intent
create_resource_policy Creates a new resource policy with the specified policy statements
create_resource_policy_statement Adds a new resource policy statement to a bot or bot alias
create_slot Creates a slot in an intent
create_slot_type Creates a custom slot type
create_test_set_discrepancy_report Create a report that describes the differences between the bot and the test set
create_upload_url Gets a pre-signed S3 write URL that you use to upload the zip archive when importing a bot or a bot locale
delete_bot Deletes all versions of a bot, including the Draft version
delete_bot_alias Deletes the specified bot alias
delete_bot_locale Removes a locale from a bot
delete_bot_replica The action to delete the replicated bot in the secondary region
delete_bot_version Deletes a specific version of a bot
delete_custom_vocabulary Removes a custom vocabulary from the specified locale in the specified bot
delete_export Removes a previous export and the associated files stored in an S3 bucket
delete_import Removes a previous import and the associated file stored in an S3 bucket
delete_intent Removes the specified intent
delete_resource_policy Removes an existing policy from a bot or bot alias
delete_resource_policy_statement Deletes a policy statement from a resource policy
delete_slot Deletes the specified slot from an intent
delete_slot_type Deletes a slot type from a bot locale
delete_test_set The action to delete the selected test set
delete_utterances Deletes stored utterances
describe_bot Provides metadata information about a bot
describe_bot_alias Get information about a specific bot alias
describe_bot_locale Describes the settings that a bot has for a specific locale
describe_bot_recommendation Provides metadata information about a bot recommendation
describe_bot_replica Monitors the bot replication status through the UI console
describe_bot_resource_generation Returns information about a request to generate a bot through natural language description, made through the StartBotResource API
describe_bot_version Provides metadata about a version of a bot
describe_custom_vocabulary_metadata Provides metadata information about a custom vocabulary
describe_export Gets information about a specific export
describe_import Gets information about a specific import
describe_intent Returns metadata about an intent
describe_resource_policy Gets the resource policy and policy revision for a bot or bot alias
describe_slot Gets metadata information about a slot
describe_slot_type Gets metadata information about a slot type
describe_test_execution Gets metadata information about the test execution
describe_test_set Gets metadata information about the test set
describe_test_set_discrepancy_report Gets metadata information about the test set discrepancy report
describe_test_set_generation Gets metadata information about the test set generation
generate_bot_element Generates sample utterances for an intent
get_test_execution_artifacts_url The pre-signed Amazon S3 URL to download the test execution result artifacts
list_aggregated_utterances Provides a list of utterances that users have sent to the bot
list_bot_aliases Gets a list of aliases for the specified bot
list_bot_alias_replicas The action to list the replicated bots created from the source bot alias
list_bot_locales Gets a list of locales for the specified bot
list_bot_recommendations Get a list of bot recommendations that meet the specified criteria
list_bot_replicas The action to list the replicated bots
list_bot_resource_generations Lists the generation requests made for a bot locale
list_bots Gets a list of available bots
list_bot_version_replicas Contains information about all the versions replication statuses applicable for Global Resiliency
list_bot_versions Gets information about all of the versions of a bot
list_built_in_intents Gets a list of built-in intents provided by Amazon Lex that you can use in your bot
list_built_in_slot_types Gets a list of built-in slot types that meet the specified criteria
list_custom_vocabulary_items Paginated list of custom vocabulary items for a given bot locale's custom vocabulary
list_exports Lists the exports for a bot, bot locale, or custom vocabulary
list_imports Lists the imports for a bot, bot locale, or custom vocabulary
list_intent_metrics Retrieves summary metrics for the intents in your bot
list_intent_paths Retrieves summary statistics for a path of intents that users take over sessions with your bot
list_intents Get a list of intents that meet the specified criteria
list_intent_stage_metrics Retrieves summary metrics for the stages within intents in your bot
list_recommended_intents Gets a list of recommended intents provided by the bot recommendation that you can use in your bot
list_session_analytics_data Retrieves a list of metadata for individual user sessions with your bot
list_session_metrics Retrieves summary metrics for the user sessions with your bot
list_slots Gets a list of slots that match the specified criteria
list_slot_types Gets a list of slot types that match the specified criteria
list_tags_for_resource Gets a list of tags associated with a resource
list_test_execution_result_items Gets a list of test execution result items
list_test_executions The list of test set executions
list_test_set_records The list of test set records
list_test_sets The list of the test sets
list_utterance_analytics_data To use this API operation, your IAM role must have permissions to perform the ListAggregatedUtterances operation, which provides access to utterance-related analytics
list_utterance_metrics To use this API operation, your IAM role must have permissions to perform the ListAggregatedUtterances operation, which provides access to utterance-related analytics
search_associated_transcripts Search for associated transcripts that meet the specified criteria
start_bot_recommendation Use this to provide your transcript data, and to start the bot recommendation process
start_bot_resource_generation Starts a request for the descriptive bot builder to generate a bot locale configuration based on the prompt you provide it
start_import Starts importing a bot, bot locale, or custom vocabulary from a zip archive that you uploaded to an S3 bucket
start_test_execution The action to start test set execution
start_test_set_generation The action to start the generation of test set
stop_bot_recommendation Stop an already running Bot Recommendation request
tag_resource Adds the specified tags to the specified resource
untag_resource Removes tags from a bot, bot alias, or bot channel
update_bot Updates the configuration of an existing bot
update_bot_alias Updates the configuration of an existing bot alias
update_bot_locale Updates the settings that a bot has for a specific locale
update_bot_recommendation Updates an existing bot recommendation request
update_export Updates the password used to protect an export zip archive
update_intent Updates the settings for an intent
update_resource_policy Replaces the existing resource policy for a bot or bot alias with a new one
update_slot Updates the settings for a slot
update_slot_type Updates the configuration of an existing slot type
update_test_set The action to update the test set

Examples

## Not run: 
svc <- lexmodelsv2()
svc$batch_create_custom_vocabulary_item(
  Foo = 123
)

## End(Not run)

Amazon Lex Runtime Service

Description

Amazon Lex provides both build and runtime endpoints. Each endpoint provides a set of operations (API). Your conversational bot uses the runtime API to understand user utterances (user input text or voice). For example, suppose a user says "I want pizza", your bot sends this input to Amazon Lex using the runtime API. Amazon Lex recognizes that the user request is for the OrderPizza intent (one of the intents defined in the bot). Then Amazon Lex engages in user conversation on behalf of the bot to elicit required information (slot values, such as pizza size and crust type), and then performs fulfillment activity (that you configured when you created the bot). You use the build-time API to create and manage your Amazon Lex bot. For a list of build-time operations, see the build-time API, .

Usage

lexruntimeservice(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- lexruntimeservice(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

delete_session Removes session information for a specified bot, alias, and user ID
get_session Returns session information for a specified bot, alias, and user ID
post_content Sends user input (text or speech) to Amazon Lex
post_text Sends user input to Amazon Lex
put_session Creates a new session or modifies an existing session with an Amazon Lex bot

Examples

## Not run: 
svc <- lexruntimeservice()
svc$delete_session(
  Foo = 123
)

## End(Not run)

Amazon Lex Runtime V2

Description

This section contains documentation for the Amazon Lex V2 Runtime V2 API operations.

Usage

lexruntimev2(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- lexruntimev2(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

delete_session Removes session information for a specified bot, alias, and user ID
get_session Returns session information for a specified bot, alias, and user
put_session Creates a new session or modifies an existing session with an Amazon Lex V2 bot
recognize_text Sends user input to Amazon Lex V2
recognize_utterance Sends user input to Amazon Lex V2

Examples

## Not run: 
svc <- lexruntimev2()
svc$delete_session(
  Foo = 123
)

## End(Not run)

Amazon Lookout for Equipment

Description

Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify anomalies in machines from sensor data for use in predictive maintenance.

Usage

lookoutequipment(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- lookoutequipment(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

create_dataset Creates a container for a collection of data being ingested for analysis
create_inference_scheduler Creates a scheduled inference
create_label Creates a label for an event
create_label_group Creates a group of labels
create_model Creates a machine learning model for data inference
create_retraining_scheduler Creates a retraining scheduler on the specified model
delete_dataset Deletes a dataset and associated artifacts
delete_inference_scheduler Deletes an inference scheduler that has been set up
delete_label Deletes a label
delete_label_group Deletes a group of labels
delete_model Deletes a machine learning model currently available for Amazon Lookout for Equipment
delete_resource_policy Deletes the resource policy attached to the resource
delete_retraining_scheduler Deletes a retraining scheduler from a model
describe_data_ingestion_job Provides information on a specific data ingestion job such as creation time, dataset ARN, and status
describe_dataset Provides a JSON description of the data in each time series dataset, including names, column names, and data types
describe_inference_scheduler Specifies information about the inference scheduler being used, including name, model, status, and associated metadata
describe_label Returns the name of the label
describe_label_group Returns information about the label group
describe_model Provides a JSON containing the overall information about a specific machine learning model, including model name and ARN, dataset, training and evaluation information, status, and so on
describe_model_version Retrieves information about a specific machine learning model version
describe_resource_policy Provides the details of a resource policy attached to a resource
describe_retraining_scheduler Provides a description of the retraining scheduler, including information such as the model name and retraining parameters
import_dataset Imports a dataset
import_model_version Imports a model that has been trained successfully
list_data_ingestion_jobs Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on
list_datasets Lists all datasets currently available in your account, filtering on the dataset name
list_inference_events Lists all inference events that have been found for the specified inference scheduler
list_inference_executions Lists all inference executions that have been performed by the specified inference scheduler
list_inference_schedulers Retrieves a list of all inference schedulers currently available for your account
list_label_groups Returns a list of the label groups
list_labels Provides a list of labels
list_models Generates a list of all models in the account, including model name and ARN, dataset, and status
list_model_versions Generates a list of all model versions for a given model, including the model version, model version ARN, and status
list_retraining_schedulers Lists all retraining schedulers in your account, filtering by model name prefix and status
list_sensor_statistics Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset
list_tags_for_resource Lists all the tags for a specified resource, including key and value
put_resource_policy Creates a resource control policy for a given resource
start_data_ingestion_job Starts a data ingestion job
start_inference_scheduler Starts an inference scheduler
start_retraining_scheduler Starts a retraining scheduler
stop_inference_scheduler Stops an inference scheduler
stop_retraining_scheduler Stops a retraining scheduler
tag_resource Associates a given tag to a resource in your account
untag_resource Removes a specific tag from a given resource
update_active_model_version Sets the active model version for a given machine learning model
update_inference_scheduler Updates an inference scheduler
update_label_group Updates the label group
update_model Updates a model in the account
update_retraining_scheduler Updates a retraining scheduler

Examples

## Not run: 
svc <- lookoutequipment()
# 
svc$create_retraining_scheduler(
  ClientToken = "sample-client-token",
  LookbackWindow = "P360D",
  ModelName = "sample-model",
  PromoteMode = "MANUAL",
  RetrainingFrequency = "P1M"
)

## End(Not run)

Amazon Lookout for Metrics

Description

This is the Amazon Lookout for Metrics API Reference. For an introduction to the service with tutorials for getting started, visit Amazon Lookout for Metrics Developer Guide.

Usage

lookoutmetrics(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- lookoutmetrics(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

activate_anomaly_detector Activates an anomaly detector
back_test_anomaly_detector Runs a backtest for anomaly detection for the specified resource
create_alert Creates an alert for an anomaly detector
create_anomaly_detector Creates an anomaly detector
create_metric_set Creates a dataset
deactivate_anomaly_detector Deactivates an anomaly detector
delete_alert Deletes an alert
delete_anomaly_detector Deletes a detector
describe_alert Describes an alert
describe_anomaly_detection_executions Returns information about the status of the specified anomaly detection jobs
describe_anomaly_detector Describes a detector
describe_metric_set Describes a dataset
detect_metric_set_config Detects an Amazon S3 dataset's file format, interval, and offset
get_anomaly_group Returns details about a group of anomalous metrics
get_data_quality_metrics Returns details about the requested data quality metrics
get_feedback Get feedback for an anomaly group
get_sample_data Returns a selection of sample records from an Amazon S3 datasource
list_alerts Lists the alerts attached to a detector
list_anomaly_detectors Lists the detectors in the current AWS Region
list_anomaly_group_related_metrics Returns a list of measures that are potential causes or effects of an anomaly group
list_anomaly_group_summaries Returns a list of anomaly groups
list_anomaly_group_time_series Gets a list of anomalous metrics for a measure in an anomaly group
list_metric_sets Lists the datasets in the current AWS Region
list_tags_for_resource Gets a list of tags for a detector, dataset, or alert
put_feedback Add feedback for an anomalous metric
tag_resource Adds tags to a detector, dataset, or alert
untag_resource Removes tags from a detector, dataset, or alert
update_alert Make changes to an existing alert
update_anomaly_detector Updates a detector
update_metric_set Updates a dataset

Examples

## Not run: 
svc <- lookoutmetrics()
svc$activate_anomaly_detector(
  Foo = 123
)

## End(Not run)

Amazon Machine Learning

Description

Definition of the public APIs exposed by Amazon Machine Learning

Usage

machinelearning(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- machinelearning(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

add_tags Adds one or more tags to an object, up to a limit of 10
create_batch_prediction Generates predictions for a group of observations
create_data_source_from_rds Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS)
create_data_source_from_redshift Creates a DataSource from a database hosted on an Amazon Redshift cluster
create_data_source_from_s3 Creates a DataSource object
create_evaluation Creates a new Evaluation of an MLModel
create_ml_model Creates a new MLModel using the DataSource and the recipe as information sources
create_realtime_endpoint Creates a real-time endpoint for the MLModel
delete_batch_prediction Assigns the DELETED status to a BatchPrediction, rendering it unusable
delete_data_source Assigns the DELETED status to a DataSource, rendering it unusable
delete_evaluation Assigns the DELETED status to an Evaluation, rendering it unusable
delete_ml_model Assigns the DELETED status to an MLModel, rendering it unusable
delete_realtime_endpoint Deletes a real time endpoint of an MLModel
delete_tags Deletes the specified tags associated with an ML object
describe_batch_predictions Returns a list of BatchPrediction operations that match the search criteria in the request
describe_data_sources Returns a list of DataSource that match the search criteria in the request
describe_evaluations Returns a list of DescribeEvaluations that match the search criteria in the request
describe_ml_models Returns a list of MLModel that match the search criteria in the request
describe_tags Describes one or more of the tags for your Amazon ML object
get_batch_prediction Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request
get_data_source Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource
get_evaluation Returns an Evaluation that includes metadata as well as the current status of the Evaluation
get_ml_model Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel
predict Generates a prediction for the observation using the specified ML Model
update_batch_prediction Updates the BatchPredictionName of a BatchPrediction
update_data_source Updates the DataSourceName of a DataSource
update_evaluation Updates the EvaluationName of an Evaluation
update_ml_model Updates the MLModelName and the ScoreThreshold of an MLModel

Examples

## Not run: 
svc <- machinelearning()
svc$add_tags(
  Foo = 123
)

## End(Not run)

AWS Panorama

Description

Overview

This is the AWS Panorama API Reference. For an introduction to the service, see What is AWS Panorama? in the AWS Panorama Developer Guide.

Usage

panorama(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- panorama(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

create_application_instance Creates an application instance and deploys it to a device
create_job_for_devices Creates a job to run on a device
create_node_from_template_job Creates a camera stream node
create_package Creates a package and storage location in an Amazon S3 access point
create_package_import_job Imports a node package
delete_device Deletes a device
delete_package Deletes a package
deregister_package_version Deregisters a package version
describe_application_instance Returns information about an application instance on a device
describe_application_instance_details Returns information about an application instance's configuration manifest
describe_device Returns information about a device
describe_device_job Returns information about a device job
describe_node Returns information about a node
describe_node_from_template_job Returns information about a job to create a camera stream node
describe_package Returns information about a package
describe_package_import_job Returns information about a package import job
describe_package_version Returns information about a package version
list_application_instance_dependencies Returns a list of application instance dependencies
list_application_instance_node_instances Returns a list of application node instances
list_application_instances Returns a list of application instances
list_devices Returns a list of devices
list_devices_jobs Returns a list of jobs
list_node_from_template_jobs Returns a list of camera stream node jobs
list_nodes Returns a list of nodes
list_package_import_jobs Returns a list of package import jobs
list_packages Returns a list of packages
list_tags_for_resource Returns a list of tags for a resource
provision_device Creates a device and returns a configuration archive
register_package_version Registers a package version
remove_application_instance Removes an application instance
signal_application_instance_node_instances Signal camera nodes to stop or resume
tag_resource Tags a resource
untag_resource Removes tags from a resource
update_device_metadata Updates a device's metadata

Examples

## Not run: 
svc <- panorama()
svc$create_application_instance(
  Foo = 123
)

## End(Not run)

Amazon Personalize

Description

Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.

Usage

personalize(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- personalize(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

create_batch_inference_job Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket
create_batch_segment_job Creates a batch segment job
create_campaign You incur campaign costs while it is active
create_data_deletion_job Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches
create_dataset Creates an empty dataset and adds it to the specified dataset group
create_dataset_export_job Creates a job that exports data from your dataset to an Amazon S3 bucket
create_dataset_group Creates an empty dataset group
create_dataset_import_job Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset
create_event_tracker Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API
create_filter Creates a recommendation filter
create_metric_attribution Creates a metric attribution
create_recommender Creates a recommender with the recipe (a Domain dataset group use case) you specify
create_schema Creates an Amazon Personalize schema from the specified schema string
create_solution By default, all new solutions use automatic training
create_solution_version Trains or retrains an active solution in a Custom dataset group
delete_campaign Removes a campaign by deleting the solution deployment
delete_dataset Deletes a dataset
delete_dataset_group Deletes a dataset group
delete_event_tracker Deletes the event tracker
delete_filter Deletes a filter
delete_metric_attribution Deletes a metric attribution
delete_recommender Deactivates and removes a recommender
delete_schema Deletes a schema
delete_solution Deletes all versions of a solution and the Solution object itself
describe_algorithm Describes the given algorithm
describe_batch_inference_job Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations
describe_batch_segment_job Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments
describe_campaign Describes the given campaign, including its status
describe_data_deletion_job Describes the data deletion job created by CreateDataDeletionJob, including the job status
describe_dataset Describes the given dataset
describe_dataset_export_job Describes the dataset export job created by CreateDatasetExportJob, including the export job status
describe_dataset_group Describes the given dataset group
describe_dataset_import_job Describes the dataset import job created by CreateDatasetImportJob, including the import job status
describe_event_tracker Describes an event tracker
describe_feature_transformation Describes the given feature transformation
describe_filter Describes a filter's properties
describe_metric_attribution Describes a metric attribution
describe_recipe Describes a recipe
describe_recommender Describes the given recommender, including its status
describe_schema Describes a schema
describe_solution Describes a solution
describe_solution_version Describes a specific version of a solution
get_solution_metrics Gets the metrics for the specified solution version
list_batch_inference_jobs Gets a list of the batch inference jobs that have been performed off of a solution version
list_batch_segment_jobs Gets a list of the batch segment jobs that have been performed off of a solution version that you specify
list_campaigns Returns a list of campaigns that use the given solution
list_data_deletion_jobs Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first
list_dataset_export_jobs Returns a list of dataset export jobs that use the given dataset
list_dataset_groups Returns a list of dataset groups
list_dataset_import_jobs Returns a list of dataset import jobs that use the given dataset
list_datasets Returns the list of datasets contained in the given dataset group
list_event_trackers Returns the list of event trackers associated with the account
list_filters Lists all filters that belong to a given dataset group
list_metric_attribution_metrics Lists the metrics for the metric attribution
list_metric_attributions Lists metric attributions
list_recipes Returns a list of available recipes
list_recommenders Returns a list of recommenders in a given Domain dataset group
list_schemas Returns the list of schemas associated with the account
list_solutions Returns a list of solutions in a given dataset group
list_solution_versions Returns a list of solution versions for the given solution
list_tags_for_resource Get a list of tags attached to a resource
start_recommender Starts a recommender that is INACTIVE
stop_recommender Stops a recommender that is ACTIVE
stop_solution_version_creation Stops creating a solution version that is in a state of CREATE_PENDING or CREATE IN_PROGRESS
tag_resource Add a list of tags to a resource
untag_resource Removes the specified tags that are attached to a resource
update_campaign Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration
update_dataset Update a dataset to replace its schema with a new or existing one
update_metric_attribution Updates a metric attribution
update_recommender Updates the recommender to modify the recommender configuration
update_solution Updates an Amazon Personalize solution to use a different automatic training configuration

Examples

## Not run: 
svc <- personalize()
svc$create_batch_inference_job(
  Foo = 123
)

## End(Not run)

Amazon Personalize Events

Description

Amazon Personalize can consume real-time user event data, such as stream or click data, and use it for model training either alone or combined with historical data. For more information see Recording item interaction events.

Usage

personalizeevents(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- personalizeevents(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

put_action_interactions Records action interaction event data
put_actions Adds one or more actions to an Actions dataset
put_events Records item interaction event data
put_items Adds one or more items to an Items dataset
put_users Adds one or more users to a Users dataset

Examples

## Not run: 
svc <- personalizeevents()
svc$put_action_interactions(
  Foo = 123
)

## End(Not run)

Amazon Personalize Runtime

Description

Amazon Personalize Runtime

Usage

personalizeruntime(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- personalizeruntime(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

get_action_recommendations Returns a list of recommended actions in sorted in descending order by prediction score
get_personalized_ranking Re-ranks a list of recommended items for the given user
get_recommendations Returns a list of recommended items

Examples

## Not run: 
svc <- personalizeruntime()
svc$get_action_recommendations(
  Foo = 123
)

## End(Not run)

Amazon Polly

Description

Amazon Polly is a web service that makes it easy to synthesize speech from text.

The Amazon Polly service provides API operations for synthesizing high-quality speech from plain text and Speech Synthesis Markup Language (SSML), along with managing pronunciations lexicons that enable you to get the best results for your application domain.

Usage

polly(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- polly(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

delete_lexicon Deletes the specified pronunciation lexicon stored in an Amazon Web Services Region
describe_voices Returns the list of voices that are available for use when requesting speech synthesis
get_lexicon Returns the content of the specified pronunciation lexicon stored in an Amazon Web Services Region
get_speech_synthesis_task Retrieves a specific SpeechSynthesisTask object based on its TaskID
list_lexicons Returns a list of pronunciation lexicons stored in an Amazon Web Services Region
list_speech_synthesis_tasks Returns a list of SpeechSynthesisTask objects ordered by their creation date
put_lexicon Stores a pronunciation lexicon in an Amazon Web Services Region
start_speech_synthesis_task Allows the creation of an asynchronous synthesis task, by starting a new SpeechSynthesisTask
synthesize_speech Synthesizes UTF-8 input, plain text or SSML, to a stream of bytes

Examples

## Not run: 
svc <- polly()
# Deletes a specified pronunciation lexicon stored in an AWS Region.
svc$delete_lexicon(
  Name = "example"
)

## End(Not run)

Amazon Rekognition

Description

This is the API Reference for Amazon Rekognition Image, Amazon Rekognition Custom Labels, Amazon Rekognition Stored Video, Amazon Rekognition Streaming Video. It provides descriptions of actions, data types, common parameters, and common errors.

Amazon Rekognition Image

Amazon Rekognition Custom Labels

Amazon Rekognition Video Stored Video

Amazon Rekognition Video Streaming Video

Usage

rekognition(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- rekognition(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

associate_faces Associates one or more faces with an existing UserID
compare_faces Compares a face in the source input image with each of the 100 largest faces detected in the target input image
copy_project_version This operation applies only to Amazon Rekognition Custom Labels
create_collection Creates a collection in an AWS Region
create_dataset This operation applies only to Amazon Rekognition Custom Labels
create_face_liveness_session This API operation initiates a Face Liveness session
create_project Creates a new Amazon Rekognition project
create_project_version Creates a new version of Amazon Rekognition project (like a Custom Labels model or a custom adapter) and begins training
create_stream_processor Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video
create_user Creates a new User within a collection specified by CollectionId
delete_collection Deletes the specified collection
delete_dataset This operation applies only to Amazon Rekognition Custom Labels
delete_faces Deletes faces from a collection
delete_project Deletes a Amazon Rekognition project
delete_project_policy This operation applies only to Amazon Rekognition Custom Labels
delete_project_version Deletes a Rekognition project model or project version, like a Amazon Rekognition Custom Labels model or a custom adapter
delete_stream_processor Deletes the stream processor identified by Name
delete_user Deletes the specified UserID within the collection
describe_collection Describes the specified collection
describe_dataset This operation applies only to Amazon Rekognition Custom Labels
describe_projects Gets information about your Rekognition projects
describe_project_versions Lists and describes the versions of an Amazon Rekognition project
describe_stream_processor Provides information about a stream processor created by CreateStreamProcessor
detect_custom_labels This operation applies only to Amazon Rekognition Custom Labels
detect_faces Detects faces within an image that is provided as input
detect_labels Detects instances of real-world entities within an image (JPEG or PNG) provided as input
detect_moderation_labels Detects unsafe content in a specified JPEG or PNG format image
detect_protective_equipment Detects Personal Protective Equipment (PPE) worn by people detected in an image
detect_text Detects text in the input image and converts it into machine-readable text
disassociate_faces Removes the association between a Face supplied in an array of FaceIds and the User
distribute_dataset_entries This operation applies only to Amazon Rekognition Custom Labels
get_celebrity_info Gets the name and additional information about a celebrity based on their Amazon Rekognition ID
get_celebrity_recognition Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition
get_content_moderation Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration
get_face_detection Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection
get_face_liveness_session_results Retrieves the results of a specific Face Liveness session
get_face_search Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch
get_label_detection Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection
get_media_analysis_job Retrieves the results for a given media analysis job
get_person_tracking Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking
get_segment_detection Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection
get_text_detection Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection
index_faces Detects faces in the input image and adds them to the specified collection
list_collections Returns list of collection IDs in your account
list_dataset_entries This operation applies only to Amazon Rekognition Custom Labels
list_dataset_labels This operation applies only to Amazon Rekognition Custom Labels
list_faces Returns metadata for faces in the specified collection
list_media_analysis_jobs Returns a list of media analysis jobs
list_project_policies This operation applies only to Amazon Rekognition Custom Labels
list_stream_processors Gets a list of stream processors that you have created with CreateStreamProcessor
list_tags_for_resource Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model
list_users Returns metadata of the User such as UserID in the specified collection
put_project_policy This operation applies only to Amazon Rekognition Custom Labels
recognize_celebrities Returns an array of celebrities recognized in the input image
search_faces For a given input face ID, searches for matching faces in the collection the face belongs to
search_faces_by_image For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces
search_users Searches for UserIDs within a collection based on a FaceId or UserId
search_users_by_image Searches for UserIDs using a supplied image
start_celebrity_recognition Starts asynchronous recognition of celebrities in a stored video
start_content_moderation Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video
start_face_detection Starts asynchronous detection of faces in a stored video
start_face_search Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video
start_label_detection Starts asynchronous detection of labels in a stored video
start_media_analysis_job Initiates a new media analysis job
start_person_tracking Starts the asynchronous tracking of a person's path in a stored video
start_project_version This operation applies only to Amazon Rekognition Custom Labels
start_segment_detection Starts asynchronous detection of segment detection in a stored video
start_stream_processor Starts processing a stream processor
start_text_detection Starts asynchronous detection of text in a stored video
stop_project_version This operation applies only to Amazon Rekognition Custom Labels
stop_stream_processor Stops a running stream processor that was created by CreateStreamProcessor
tag_resource Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model
untag_resource Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model
update_dataset_entries This operation applies only to Amazon Rekognition Custom Labels
update_stream_processor Allows you to update a stream processor

Examples

## Not run: 
svc <- rekognition()
# This operation associates one or more faces with an existing UserID.
svc$associate_faces(
  ClientRequestToken = "550e8400-e29b-41d4-a716-446655440002",
  CollectionId = "MyCollection",
  FaceIds = list(
    "f5817d37-94f6-4335-bfee-6cf79a3d806e",
    "851cb847-dccc-4fea-9309-9f4805967855",
    "35ebbb41-7f67-4263-908d-dd0ecba05ab9"
  ),
  UserId = "DemoUser",
  UserMatchThreshold = 70L
)

## End(Not run)

Amazon SageMaker Service

Description

Provides APIs for creating and managing SageMaker resources.

Other Resources:

Usage

sagemaker(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- sagemaker(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

add_association Creates an association between the source and the destination
add_tags Adds or overwrites one or more tags for the specified SageMaker resource
associate_trial_component Associates a trial component with a trial
batch_describe_model_package This action batch describes a list of versioned model packages
create_action Creates an action
create_algorithm Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace
create_app Creates a running app for the specified UserProfile
create_app_image_config Creates a configuration for running a SageMaker image as a KernelGateway app
create_artifact Creates an artifact
create_auto_ml_job Creates an Autopilot job also referred to as Autopilot experiment or AutoML job
create_auto_ml_job_v2 Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2
create_cluster Creates a SageMaker HyperPod cluster
create_code_repository Creates a Git repository as a resource in your SageMaker account
create_compilation_job Starts a model compilation job
create_context Creates a context
create_data_quality_job_definition Creates a definition for a job that monitors data quality and drift
create_device_fleet Creates a device fleet
create_domain Creates a Domain
create_edge_deployment_plan Creates an edge deployment plan, consisting of multiple stages
create_edge_deployment_stage Creates a new stage in an existing edge deployment plan
create_edge_packaging_job Starts a SageMaker Edge Manager model packaging job
create_endpoint Creates an endpoint using the endpoint configuration specified in the request
create_endpoint_config Creates an endpoint configuration that SageMaker hosting services uses to deploy models
create_experiment Creates a SageMaker experiment
create_feature_group Create a new FeatureGroup
create_flow_definition Creates a flow definition
create_hub Create a hub
create_hub_content_reference Create a hub content reference in order to add a model in the JumpStart public hub to a private hub
create_human_task_ui Defines the settings you will use for the human review workflow user interface
create_hyper_parameter_tuning_job Starts a hyperparameter tuning job
create_image Creates a custom SageMaker image
create_image_version Creates a version of the SageMaker image specified by ImageName
create_inference_component Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint
create_inference_experiment Creates an inference experiment using the configurations specified in the request
create_inference_recommendations_job Starts a recommendation job
create_labeling_job Creates a job that uses workers to label the data objects in your input dataset
create_mlflow_tracking_server Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store
create_model Creates a model in SageMaker
create_model_bias_job_definition Creates the definition for a model bias job
create_model_card Creates an Amazon SageMaker Model Card
create_model_card_export_job Creates an Amazon SageMaker Model Card export job
create_model_explainability_job_definition Creates the definition for a model explainability job
create_model_package Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group
create_model_package_group Creates a model group
create_model_quality_job_definition Creates a definition for a job that monitors model quality and drift
create_monitoring_schedule Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint
create_notebook_instance Creates an SageMaker notebook instance
create_notebook_instance_lifecycle_config Creates a lifecycle configuration that you can associate with a notebook instance
create_optimization_job Creates a job that optimizes a model for inference performance
create_pipeline Creates a pipeline using a JSON pipeline definition
create_presigned_domain_url Creates a URL for a specified UserProfile in a Domain
create_presigned_mlflow_tracking_server_url Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server
create_presigned_notebook_instance_url Returns a URL that you can use to connect to the Jupyter server from a notebook instance
create_processing_job Creates a processing job
create_project Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model
create_space Creates a private space or a space used for real time collaboration in a domain
create_studio_lifecycle_config Creates a new Amazon SageMaker Studio Lifecycle Configuration
create_training_job Starts a model training job
create_transform_job Starts a transform job
create_trial Creates an SageMaker trial
create_trial_component Creates a trial component, which is a stage of a machine learning trial
create_user_profile Creates a user profile
create_workforce Use this operation to create a workforce
create_workteam Creates a new work team for labeling your data
delete_action Deletes an action
delete_algorithm Removes the specified algorithm from your account
delete_app Used to stop and delete an app
delete_app_image_config Deletes an AppImageConfig
delete_artifact Deletes an artifact
delete_association Deletes an association
delete_cluster Delete a SageMaker HyperPod cluster
delete_code_repository Deletes the specified Git repository from your account
delete_compilation_job Deletes the specified compilation job
delete_context Deletes an context
delete_data_quality_job_definition Deletes a data quality monitoring job definition
delete_device_fleet Deletes a fleet
delete_domain Used to delete a domain
delete_edge_deployment_plan Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan
delete_edge_deployment_stage Delete a stage in an edge deployment plan if (and only if) the stage is inactive
delete_endpoint Deletes an endpoint
delete_endpoint_config Deletes an endpoint configuration
delete_experiment Deletes an SageMaker experiment
delete_feature_group Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup
delete_flow_definition Deletes the specified flow definition
delete_hub Delete a hub
delete_hub_content Delete the contents of a hub
delete_hub_content_reference Delete a hub content reference in order to remove a model from a private hub
delete_human_task_ui Use this operation to delete a human task user interface (worker task template)
delete_hyper_parameter_tuning_job Deletes a hyperparameter tuning job
delete_image Deletes a SageMaker image and all versions of the image
delete_image_version Deletes a version of a SageMaker image
delete_inference_component Deletes an inference component
delete_inference_experiment Deletes an inference experiment
delete_mlflow_tracking_server Deletes an MLflow Tracking Server
delete_model Deletes a model
delete_model_bias_job_definition Deletes an Amazon SageMaker model bias job definition
delete_model_card Deletes an Amazon SageMaker Model Card
delete_model_explainability_job_definition Deletes an Amazon SageMaker model explainability job definition
delete_model_package Deletes a model package
delete_model_package_group Deletes the specified model group
delete_model_package_group_policy Deletes a model group resource policy
delete_model_quality_job_definition Deletes the secified model quality monitoring job definition
delete_monitoring_schedule Deletes a monitoring schedule
delete_notebook_instance Deletes an SageMaker notebook instance
delete_notebook_instance_lifecycle_config Deletes a notebook instance lifecycle configuration
delete_optimization_job Deletes an optimization job
delete_pipeline Deletes a pipeline if there are no running instances of the pipeline
delete_project Delete the specified project
delete_space Used to delete a space
delete_studio_lifecycle_config Deletes the Amazon SageMaker Studio Lifecycle Configuration
delete_tags Deletes the specified tags from an SageMaker resource
delete_trial Deletes the specified trial
delete_trial_component Deletes the specified trial component
delete_user_profile Deletes a user profile
delete_workforce Use this operation to delete a workforce
delete_workteam Deletes an existing work team
deregister_devices Deregisters the specified devices
describe_action Describes an action
describe_algorithm Returns a description of the specified algorithm that is in your account
describe_app Describes the app
describe_app_image_config Describes an AppImageConfig
describe_artifact Describes an artifact
describe_auto_ml_job Returns information about an AutoML job created by calling CreateAutoMLJob
describe_auto_ml_job_v2 Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob
describe_cluster Retrieves information of a SageMaker HyperPod cluster
describe_cluster_node Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster
describe_code_repository Gets details about the specified Git repository
describe_compilation_job Returns information about a model compilation job
describe_context Describes a context
describe_data_quality_job_definition Gets the details of a data quality monitoring job definition
describe_device Describes the device
describe_device_fleet A description of the fleet the device belongs to
describe_domain The description of the domain
describe_edge_deployment_plan Describes an edge deployment plan with deployment status per stage
describe_edge_packaging_job A description of edge packaging jobs
describe_endpoint Returns the description of an endpoint
describe_endpoint_config Returns the description of an endpoint configuration created using the CreateEndpointConfig API
describe_experiment Provides a list of an experiment's properties
describe_feature_group Use this operation to describe a FeatureGroup
describe_feature_metadata Shows the metadata for a feature within a feature group
describe_flow_definition Returns information about the specified flow definition
describe_hub Describes a hub
describe_hub_content Describe the content of a hub
describe_human_task_ui Returns information about the requested human task user interface (worker task template)
describe_hyper_parameter_tuning_job Returns a description of a hyperparameter tuning job, depending on the fields selected
describe_image Describes a SageMaker image
describe_image_version Describes a version of a SageMaker image
describe_inference_component Returns information about an inference component
describe_inference_experiment Returns details about an inference experiment
describe_inference_recommendations_job Provides the results of the Inference Recommender job
describe_labeling_job Gets information about a labeling job
describe_lineage_group Provides a list of properties for the requested lineage group
describe_mlflow_tracking_server Returns information about an MLflow Tracking Server
describe_model Describes a model that you created using the CreateModel API
describe_model_bias_job_definition Returns a description of a model bias job definition
describe_model_card Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card
describe_model_card_export_job Describes an Amazon SageMaker Model Card export job
describe_model_explainability_job_definition Returns a description of a model explainability job definition
describe_model_package Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace
describe_model_package_group Gets a description for the specified model group
describe_model_quality_job_definition Returns a description of a model quality job definition
describe_monitoring_schedule Describes the schedule for a monitoring job
describe_notebook_instance Returns information about a notebook instance
describe_notebook_instance_lifecycle_config Returns a description of a notebook instance lifecycle configuration
describe_optimization_job Provides the properties of the specified optimization job
describe_pipeline Describes the details of a pipeline
describe_pipeline_definition_for_execution Describes the details of an execution's pipeline definition
describe_pipeline_execution Describes the details of a pipeline execution
describe_processing_job Returns a description of a processing job
describe_project Describes the details of a project
describe_space Describes the space
describe_studio_lifecycle_config Describes the Amazon SageMaker Studio Lifecycle Configuration
describe_subscribed_workteam Gets information about a work team provided by a vendor
describe_training_job Returns information about a training job
describe_transform_job Returns information about a transform job
describe_trial Provides a list of a trial's properties
describe_trial_component Provides a list of a trials component's properties
describe_user_profile Describes a user profile
describe_workforce Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs)
describe_workteam Gets information about a specific work team
disable_sagemaker_servicecatalog_portfolio Disables using Service Catalog in SageMaker
disassociate_trial_component Disassociates a trial component from a trial
enable_sagemaker_servicecatalog_portfolio Enables using Service Catalog in SageMaker
get_device_fleet_report Describes a fleet
get_lineage_group_policy The resource policy for the lineage group
get_model_package_group_policy Gets a resource policy that manages access for a model group
get_sagemaker_servicecatalog_portfolio_status Gets the status of Service Catalog in SageMaker
get_scaling_configuration_recommendation Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job
get_search_suggestions An auto-complete API for the search functionality in the SageMaker console
import_hub_content Import hub content
list_actions Lists the actions in your account and their properties
list_algorithms Lists the machine learning algorithms that have been created
list_aliases Lists the aliases of a specified image or image version
list_app_image_configs Lists the AppImageConfigs in your account and their properties
list_apps Lists apps
list_artifacts Lists the artifacts in your account and their properties
list_associations Lists the associations in your account and their properties
list_auto_ml_jobs Request a list of jobs
list_candidates_for_auto_ml_job List the candidates created for the job
list_cluster_nodes Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster
list_clusters Retrieves the list of SageMaker HyperPod clusters
list_code_repositories Gets a list of the Git repositories in your account
list_compilation_jobs Lists model compilation jobs that satisfy various filters
list_contexts Lists the contexts in your account and their properties
list_data_quality_job_definitions Lists the data quality job definitions in your account
list_device_fleets Returns a list of devices in the fleet
list_devices A list of devices
list_domains Lists the domains
list_edge_deployment_plans Lists all edge deployment plans
list_edge_packaging_jobs Returns a list of edge packaging jobs
list_endpoint_configs Lists endpoint configurations
list_endpoints Lists endpoints
list_experiments Lists all the experiments in your account
list_feature_groups List FeatureGroups based on given filter and order
list_flow_definitions Returns information about the flow definitions in your account
list_hub_contents List the contents of a hub
list_hub_content_versions List hub content versions
list_hubs List all existing hubs
list_human_task_uis Returns information about the human task user interfaces in your account
list_hyper_parameter_tuning_jobs Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account
list_images Lists the images in your account and their properties
list_image_versions Lists the versions of a specified image and their properties
list_inference_components Lists the inference components in your account and their properties
list_inference_experiments Returns the list of all inference experiments
list_inference_recommendations_jobs Lists recommendation jobs that satisfy various filters
list_inference_recommendations_job_steps Returns a list of the subtasks for an Inference Recommender job
list_labeling_jobs Gets a list of labeling jobs
list_labeling_jobs_for_workteam Gets a list of labeling jobs assigned to a specified work team
list_lineage_groups A list of lineage groups shared with your Amazon Web Services account
list_mlflow_tracking_servers Lists all MLflow Tracking Servers
list_model_bias_job_definitions Lists model bias jobs definitions that satisfy various filters
list_model_card_export_jobs List the export jobs for the Amazon SageMaker Model Card
list_model_cards List existing model cards
list_model_card_versions List existing versions of an Amazon SageMaker Model Card
list_model_explainability_job_definitions Lists model explainability job definitions that satisfy various filters
list_model_metadata Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos
list_model_package_groups Gets a list of the model groups in your Amazon Web Services account
list_model_packages Lists the model packages that have been created
list_model_quality_job_definitions Gets a list of model quality monitoring job definitions in your account
list_models Lists models created with the CreateModel API
list_monitoring_alert_history Gets a list of past alerts in a model monitoring schedule
list_monitoring_alerts Gets the alerts for a single monitoring schedule
list_monitoring_executions Returns list of all monitoring job executions
list_monitoring_schedules Returns list of all monitoring schedules
list_notebook_instance_lifecycle_configs Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API
list_notebook_instances Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region
list_optimization_jobs Lists the optimization jobs in your account and their properties
list_pipeline_executions Gets a list of the pipeline executions
list_pipeline_execution_steps Gets a list of PipeLineExecutionStep objects
list_pipeline_parameters_for_execution Gets a list of parameters for a pipeline execution
list_pipelines Gets a list of pipelines
list_processing_jobs Lists processing jobs that satisfy various filters
list_projects Gets a list of the projects in an Amazon Web Services account
list_resource_catalogs Lists Amazon SageMaker Catalogs based on given filters and orders
list_spaces Lists spaces
list_stage_devices Lists devices allocated to the stage, containing detailed device information and deployment status
list_studio_lifecycle_configs Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account
list_subscribed_workteams Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace
list_tags Returns the tags for the specified SageMaker resource
list_training_jobs Lists training jobs
list_training_jobs_for_hyper_parameter_tuning_job Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched
list_transform_jobs Lists transform jobs
list_trial_components Lists the trial components in your account
list_trials Lists the trials in your account
list_user_profiles Lists user profiles
list_workforces Use this operation to list all private and vendor workforces in an Amazon Web Services Region
list_workteams Gets a list of private work teams that you have defined in a region
put_model_package_group_policy Adds a resouce policy to control access to a model group
query_lineage Use this action to inspect your lineage and discover relationships between entities
register_devices Register devices
render_ui_template Renders the UI template so that you can preview the worker's experience
retry_pipeline_execution Retry the execution of the pipeline
search Finds SageMaker resources that match a search query
send_pipeline_execution_step_failure Notifies the pipeline that the execution of a callback step failed, along with a message describing why
send_pipeline_execution_step_success Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters
start_edge_deployment_stage Starts a stage in an edge deployment plan
start_inference_experiment Starts an inference experiment
start_mlflow_tracking_server Programmatically start an MLflow Tracking Server
start_monitoring_schedule Starts a previously stopped monitoring schedule
start_notebook_instance Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume
start_pipeline_execution Starts a pipeline execution
stop_auto_ml_job A method for forcing a running job to shut down
stop_compilation_job Stops a model compilation job
stop_edge_deployment_stage Stops a stage in an edge deployment plan
stop_edge_packaging_job Request to stop an edge packaging job
stop_hyper_parameter_tuning_job Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched
stop_inference_experiment Stops an inference experiment
stop_inference_recommendations_job Stops an Inference Recommender job
stop_labeling_job Stops a running labeling job
stop_mlflow_tracking_server Programmatically stop an MLflow Tracking Server
stop_monitoring_schedule Stops a previously started monitoring schedule
stop_notebook_instance Terminates the ML compute instance
stop_optimization_job Ends a running inference optimization job
stop_pipeline_execution Stops a pipeline execution
stop_processing_job Stops a processing job
stop_training_job Stops a training job
stop_transform_job Stops a batch transform job
update_action Updates an action
update_app_image_config Updates the properties of an AppImageConfig
update_artifact Updates an artifact
update_cluster Updates a SageMaker HyperPod cluster
update_cluster_software Updates the platform software of a SageMaker HyperPod cluster for security patching
update_code_repository Updates the specified Git repository with the specified values
update_context Updates a context
update_device_fleet Updates a fleet of devices
update_devices Updates one or more devices in a fleet
update_domain Updates the default settings for new user profiles in the domain
update_endpoint Deploys the EndpointConfig specified in the request to a new fleet of instances
update_endpoint_weights_and_capacities Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint
update_experiment Adds, updates, or removes the description of an experiment
update_feature_group Updates the feature group by either adding features or updating the online store configuration
update_feature_metadata Updates the description and parameters of the feature group
update_hub Update a hub
update_image Updates the properties of a SageMaker image
update_image_version Updates the properties of a SageMaker image version
update_inference_component Updates an inference component
update_inference_component_runtime_config Runtime settings for a model that is deployed with an inference component
update_inference_experiment Updates an inference experiment that you created
update_mlflow_tracking_server Updates properties of an existing MLflow Tracking Server
update_model_card Update an Amazon SageMaker Model Card
update_model_package Updates a versioned model
update_monitoring_alert Update the parameters of a model monitor alert
update_monitoring_schedule Updates a previously created schedule
update_notebook_instance Updates a notebook instance
update_notebook_instance_lifecycle_config Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API
update_pipeline Updates a pipeline
update_pipeline_execution Updates a pipeline execution
update_project Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model
update_space Updates the settings of a space
update_training_job Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length
update_trial Updates the display name of a trial
update_trial_component Updates one or more properties of a trial component
update_user_profile Updates a user profile
update_workforce Use this operation to update your workforce
update_workteam Updates an existing work team with new member definitions or description

Examples

## Not run: 
svc <- sagemaker()
svc$add_association(
  Foo = 123
)

## End(Not run)

Amazon Sagemaker Edge Manager

Description

SageMaker Edge Manager dataplane service for communicating with active agents.

Usage

sagemakeredgemanager(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- sagemakeredgemanager(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

get_deployments Use to get the active deployments from a device
get_device_registration Use to check if a device is registered with SageMaker Edge Manager
send_heartbeat Use to get the current status of devices registered on SageMaker Edge Manager

Examples

## Not run: 
svc <- sagemakeredgemanager()
svc$get_deployments(
  Foo = 123
)

## End(Not run)

Amazon SageMaker Feature Store Runtime

Description

Contains all data plane API operations and data types for the Amazon SageMaker Feature Store. Use this API to put, delete, and retrieve (get) features from a feature store.

Use the following operations to configure your OnlineStore and OfflineStore features, and to create and manage feature groups:

Usage

sagemakerfeaturestoreruntime(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- sagemakerfeaturestoreruntime(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

batch_get_record Retrieves a batch of Records from a FeatureGroup
delete_record Deletes a Record from a FeatureGroup in the OnlineStore
get_record Use for OnlineStore serving from a FeatureStore
put_record The PutRecord API is used to ingest a list of Records into your feature group

Examples

## Not run: 
svc <- sagemakerfeaturestoreruntime()
svc$batch_get_record(
  Foo = 123
)

## End(Not run)

Amazon SageMaker geospatial capabilities

Description

Provides APIs for creating and managing SageMaker geospatial resources.

Usage

sagemakergeospatialcapabilities(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- sagemakergeospatialcapabilities(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

delete_earth_observation_job Use this operation to delete an Earth Observation job
delete_vector_enrichment_job Use this operation to delete a Vector Enrichment job
export_earth_observation_job Use this operation to export results of an Earth Observation job and optionally source images used as input to the EOJ to an Amazon S3 location
export_vector_enrichment_job Use this operation to copy results of a Vector Enrichment job to an Amazon S3 location
get_earth_observation_job Get the details for a previously initiated Earth Observation job
get_raster_data_collection Use this operation to get details of a specific raster data collection
get_tile Gets a web mercator tile for the given Earth Observation job
get_vector_enrichment_job Retrieves details of a Vector Enrichment Job for a given job Amazon Resource Name (ARN)
list_earth_observation_jobs Use this operation to get a list of the Earth Observation jobs associated with the calling Amazon Web Services account
list_raster_data_collections Use this operation to get raster data collections
list_tags_for_resource Lists the tags attached to the resource
list_vector_enrichment_jobs Retrieves a list of vector enrichment jobs
search_raster_data_collection Allows you run image query on a specific raster data collection to get a list of the satellite imagery matching the selected filters
start_earth_observation_job Use this operation to create an Earth observation job
start_vector_enrichment_job Creates a Vector Enrichment job for the supplied job type
stop_earth_observation_job Use this operation to stop an existing earth observation job
stop_vector_enrichment_job Stops the Vector Enrichment job for a given job ARN
tag_resource The resource you want to tag
untag_resource The resource you want to untag

Examples

## Not run: 
svc <- sagemakergeospatialcapabilities()
svc$delete_earth_observation_job(
  Foo = 123
)

## End(Not run)

Amazon SageMaker Metrics Service

Description

Contains all data plane API operations and data types for Amazon SageMaker Metrics. Use these APIs to put and retrieve (get) features related to your training run.

Usage

sagemakermetrics(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- sagemakermetrics(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

batch_put_metrics Used to ingest training metrics into SageMaker

Examples

## Not run: 
svc <- sagemakermetrics()
svc$batch_put_metrics(
  Foo = 123
)

## End(Not run)

Amazon SageMaker Runtime

Description

The Amazon SageMaker runtime API.

Usage

sagemakerruntime(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- sagemakerruntime(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

invoke_endpoint After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint
invoke_endpoint_async After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint in an asynchronous manner
invoke_endpoint_with_response_stream Invokes a model at the specified endpoint to return the inference response as a stream

Examples

## Not run: 
svc <- sagemakerruntime()
svc$invoke_endpoint(
  Foo = 123
)

## End(Not run)

Amazon Textract

Description

Amazon Textract detects and analyzes text in documents and converts it into machine-readable text. This is the API reference documentation for Amazon Textract.

Usage

textract(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- textract(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

analyze_document Analyzes an input document for relationships between detected items
analyze_expense AnalyzeExpense synchronously analyzes an input document for financially related relationships between text
analyze_id Analyzes identity documents for relevant information
create_adapter Creates an adapter, which can be fine-tuned for enhanced performance on user provided documents
create_adapter_version Creates a new version of an adapter
delete_adapter Deletes an Amazon Textract adapter
delete_adapter_version Deletes an Amazon Textract adapter version
detect_document_text Detects text in the input document
get_adapter Gets configuration information for an adapter specified by an AdapterId, returning information on AdapterName, Description, CreationTime, AutoUpdate status, and FeatureTypes
get_adapter_version Gets configuration information for the specified adapter version, including: AdapterId, AdapterVersion, FeatureTypes, Status, StatusMessage, DatasetConfig, KMSKeyId, OutputConfig, Tags and EvaluationMetrics
get_document_analysis Gets the results for an Amazon Textract asynchronous operation that analyzes text in a document
get_document_text_detection Gets the results for an Amazon Textract asynchronous operation that detects text in a document
get_expense_analysis Gets the results for an Amazon Textract asynchronous operation that analyzes invoices and receipts
get_lending_analysis Gets the results for an Amazon Textract asynchronous operation that analyzes text in a lending document
get_lending_analysis_summary Gets summarized results for the StartLendingAnalysis operation, which analyzes text in a lending document
list_adapters Lists all adapters that match the specified filtration criteria
list_adapter_versions List all version of an adapter that meet the specified filtration criteria
list_tags_for_resource Lists all tags for an Amazon Textract resource
start_document_analysis Starts the asynchronous analysis of an input document for relationships between detected items such as key-value pairs, tables, and selection elements
start_document_text_detection Starts the asynchronous detection of text in a document
start_expense_analysis Starts the asynchronous analysis of invoices or receipts for data like contact information, items purchased, and vendor names
start_lending_analysis Starts the classification and analysis of an input document
tag_resource Adds one or more tags to the specified resource
untag_resource Removes any tags with the specified keys from the specified resource
update_adapter Update the configuration for an adapter

Examples

## Not run: 
svc <- textract()
svc$analyze_document(
  Foo = 123
)

## End(Not run)

Amazon Transcribe Service

Description

Amazon Transcribe offers three main types of batch transcription: Standard, Medical, and Call Analytics.

  • Standard transcriptions are the most common option. Refer to for details.

  • Medical transcriptions are tailored to medical professionals and incorporate medical terms. A common use case for this service is transcribing doctor-patient dialogue into after-visit notes. Refer to for details.

  • Call Analytics transcriptions are designed for use with call center audio on two different channels; if you're looking for insight into customer service calls, use this option. Refer to for details.

Usage

transcribeservice(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- transcribeservice(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

create_call_analytics_category Creates a new Call Analytics category
create_language_model Creates a new custom language model
create_medical_vocabulary Creates a new custom medical vocabulary
create_vocabulary Creates a new custom vocabulary
create_vocabulary_filter Creates a new custom vocabulary filter
delete_call_analytics_category Deletes a Call Analytics category
delete_call_analytics_job Deletes a Call Analytics job
delete_language_model Deletes a custom language model
delete_medical_scribe_job Deletes a Medical Scribe job
delete_medical_transcription_job Deletes a medical transcription job
delete_medical_vocabulary Deletes a custom medical vocabulary
delete_transcription_job Deletes a transcription job
delete_vocabulary Deletes a custom vocabulary
delete_vocabulary_filter Deletes a custom vocabulary filter
describe_language_model Provides information about the specified custom language model
get_call_analytics_category Provides information about the specified Call Analytics category
get_call_analytics_job Provides information about the specified Call Analytics job
get_medical_scribe_job Provides information about the specified Medical Scribe job
get_medical_transcription_job Provides information about the specified medical transcription job
get_medical_vocabulary Provides information about the specified custom medical vocabulary
get_transcription_job Provides information about the specified transcription job
get_vocabulary Provides information about the specified custom vocabulary
get_vocabulary_filter Provides information about the specified custom vocabulary filter
list_call_analytics_categories Provides a list of Call Analytics categories, including all rules that make up each category
list_call_analytics_jobs Provides a list of Call Analytics jobs that match the specified criteria
list_language_models Provides a list of custom language models that match the specified criteria
list_medical_scribe_jobs Provides a list of Medical Scribe jobs that match the specified criteria
list_medical_transcription_jobs Provides a list of medical transcription jobs that match the specified criteria
list_medical_vocabularies Provides a list of custom medical vocabularies that match the specified criteria
list_tags_for_resource Lists all tags associated with the specified transcription job, vocabulary, model, or resource
list_transcription_jobs Provides a list of transcription jobs that match the specified criteria
list_vocabularies Provides a list of custom vocabularies that match the specified criteria
list_vocabulary_filters Provides a list of custom vocabulary filters that match the specified criteria
start_call_analytics_job Transcribes the audio from a customer service call and applies any additional Request Parameters you choose to include in your request
start_medical_scribe_job Transcribes patient-clinician conversations and generates clinical notes
start_medical_transcription_job Transcribes the audio from a medical dictation or conversation and applies any additional Request Parameters you choose to include in your request
start_transcription_job Transcribes the audio from a media file and applies any additional Request Parameters you choose to include in your request
tag_resource Adds one or more custom tags, each in the form of a key:value pair, to the specified resource
untag_resource Removes the specified tags from the specified Amazon Transcribe resource
update_call_analytics_category Updates the specified Call Analytics category with new rules
update_medical_vocabulary Updates an existing custom medical vocabulary with new values
update_vocabulary Updates an existing custom vocabulary with new values
update_vocabulary_filter Updates an existing custom vocabulary filter with a new list of words

Examples

## Not run: 
svc <- transcribeservice()
svc$create_call_analytics_category(
  Foo = 123
)

## End(Not run)

Amazon Translate

Description

Provides translation of the input content from the source language to the target language.

Usage

translate(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- translate(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

create_parallel_data Creates a parallel data resource in Amazon Translate by importing an input file from Amazon S3
delete_parallel_data Deletes a parallel data resource in Amazon Translate
delete_terminology A synchronous action that deletes a custom terminology
describe_text_translation_job Gets the properties associated with an asynchronous batch translation job including name, ID, status, source and target languages, input/output S3 buckets, and so on
get_parallel_data Provides information about a parallel data resource
get_terminology Retrieves a custom terminology
import_terminology Creates or updates a custom terminology, depending on whether one already exists for the given terminology name
list_languages Provides a list of languages (RFC-5646 codes and names) that Amazon Translate supports
list_parallel_data Provides a list of your parallel data resources in Amazon Translate
list_tags_for_resource Lists all tags associated with a given Amazon Translate resource
list_terminologies Provides a list of custom terminologies associated with your account
list_text_translation_jobs Gets a list of the batch translation jobs that you have submitted
start_text_translation_job Starts an asynchronous batch translation job
stop_text_translation_job Stops an asynchronous batch translation job that is in progress
tag_resource Associates a specific tag with a resource
translate_document Translates the input document from the source language to the target language
translate_text Translates input text from the source language to the target language
untag_resource Removes a specific tag associated with an Amazon Translate resource
update_parallel_data Updates a previously created parallel data resource by importing a new input file from Amazon S3

Examples

## Not run: 
svc <- translate()
svc$create_parallel_data(
  Foo = 123
)

## End(Not run)

Amazon Voice ID

Description

Amazon Connect Voice ID provides real-time caller authentication and fraud risk detection, which make voice interactions in contact centers more secure and efficient.

Usage

voiceid(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

  • credentials:

    • creds:

      • access_key_id: AWS access key ID

      • secret_access_key: AWS secret access key

      • session_token: AWS temporary session token

    • profile: The name of a profile to use. If not given, then the default profile is used.

    • anonymous: Set anonymous credentials.

  • endpoint: The complete URL to use for the constructed client.

  • region: The AWS Region used in instantiating the client.

  • close_connection: Immediately close all HTTP connections.

  • timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

  • s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

  • sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- voiceid(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

associate_fraudster Associates the fraudsters with the watchlist specified in the same domain
create_domain Creates a domain that contains all Amazon Connect Voice ID data, such as speakers, fraudsters, customer audio, and voiceprints
create_watchlist Creates a watchlist that fraudsters can be a part of
delete_domain Deletes the specified domain from Voice ID
delete_fraudster Deletes the specified fraudster from Voice ID
delete_speaker Deletes the specified speaker from Voice ID
delete_watchlist Deletes the specified watchlist from Voice ID
describe_domain Describes the specified domain
describe_fraudster Describes the specified fraudster
describe_fraudster_registration_job Describes the specified fraudster registration job
describe_speaker Describes the specified speaker
describe_speaker_enrollment_job Describes the specified speaker enrollment job
describe_watchlist Describes the specified watchlist
disassociate_fraudster Disassociates the fraudsters from the watchlist specified
evaluate_session Evaluates a specified session based on audio data accumulated during a streaming Amazon Connect Voice ID call
list_domains Lists all the domains in the Amazon Web Services account
list_fraudster_registration_jobs Lists all the fraudster registration jobs in the domain with the given JobStatus
list_fraudsters Lists all fraudsters in a specified watchlist or domain
list_speaker_enrollment_jobs Lists all the speaker enrollment jobs in the domain with the specified JobStatus
list_speakers Lists all speakers in a specified domain
list_tags_for_resource Lists all tags associated with a specified Voice ID resource
list_watchlists Lists all watchlists in a specified domain
opt_out_speaker Opts out a speaker from Voice ID
start_fraudster_registration_job Starts a new batch fraudster registration job using provided details
start_speaker_enrollment_job Starts a new batch speaker enrollment job using specified details
tag_resource Tags a Voice ID resource with the provided list of tags
untag_resource Removes specified tags from a specified Amazon Connect Voice ID resource
update_domain Updates the specified domain
update_watchlist Updates the specified watchlist

Examples

## Not run: 
svc <- voiceid()
svc$associate_fraudster(
  Foo = 123
)

## End(Not run)