paws.machine.learning

Amazon Comprehend

Determines the dominant language of the input text for a batch of documents

Inspects the text of a batch of documents for named entities and returns information about them

Detects the key noun phrases found in a batch of documents

Inspects a batch of documents and returns an inference of the prevailing sentiment, POSITIVE, NEUTRAL, MIXED, or NEGATIVE, in each one

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

Creates a new document classification request to analyze a single document in real-time, using a previously created and trained custom model and an endpoint

Creates a new document classifier that you can use to categorize documents

Creates a model-specific endpoint for synchronous inference for a previously trained custom model

Creates an entity recognizer using submitted files

Deletes a previously created document classifier

Deletes a model-specific endpoint for a previously-trained custom model

Deletes an entity recognizer

Gets the properties associated with a document classification job

Gets the properties associated with a document classifier

Gets the properties associated with a dominant language detection job

Gets the properties associated with a specific endpoint

Gets the properties associated with an entities detection job

Provides details about an entity recognizer including status, S3 buckets containing training data, recognizer metadata, metrics, and so on

Gets the status and details of an events detection job

Gets the properties associated with a key phrases detection job

Gets the properties associated with a PII entities detection job

Gets the properties associated with a sentiment detection job

Gets the properties associated with a topic detection job

Determines the dominant language of the input text

Inspects text for named entities, and returns information about them

Detects the key noun phrases found in the text

Inspects the input text for entities that contain personally identifiable information (PII) and returns information about them

Inspects text and returns an inference of the prevailing sentiment (POSITIVE, NEUTRAL, MIXED, or NEGATIVE)

Inspects text for syntax and the part of speech of words in the document

Gets a list of the documentation classification jobs that you have submitted

Gets a list of the document classifiers that you have created

Gets a list of the dominant language detection jobs that you have submitted

Gets a list of all existing endpoints that you've created

Gets a list of the entity detection jobs that you have submitted

Gets a list of the properties of all entity recognizers that you created, including recognizers currently in training

Gets a list of the events detection jobs that you have submitted

Get a list of key phrase detection jobs that you have submitted

Gets a list of the PII entity detection jobs that you have submitted

Gets a list of sentiment detection jobs that you have submitted

Lists all tags associated with a given Amazon Comprehend resource

Gets a list of the topic detection jobs that you have submitted

Starts an asynchronous document classification job

Starts an asynchronous dominant language detection job for a collection of documents

Starts an asynchronous entity detection job for a collection of documents

Starts an asynchronous event detection job for a collection of documents

Starts an asynchronous key phrase detection job for a collection of documents

Starts an asynchronous PII entity detection job for a collection of documents

Starts an asynchronous sentiment detection job for a collection of documents

Starts an asynchronous topic detection job

Stops a dominant language detection job in progress

Stops an entities detection job in progress

Stops an events detection job in progress

Stops a key phrases detection job in progress

Stops a PII entities detection job in progress

Stops a sentiment detection job in progress

Stops a document classifier training job while in progress

Stops an entity recognizer training job while in progress

Associates a specific tag with an Amazon Comprehend resource

Removes a specific tag associated with an Amazon Comprehend resource

Updates information about the specified endpoint

AWS Comprehend Medical

Gets the properties associated with a medical entities detection job

Gets the properties associated with an InferICD10CM job

Gets the properties associated with a protected health information (PHI) detection job

Gets the properties associated with an InferRxNorm job

The DetectEntities operation is deprecated

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

Inspects the clinical text for protected health information (PHI) entities and returns the entity category, location, and confidence score for each entity

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

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

Gets a list of medical entity detection jobs that you have submitted

Gets a list of InferICD10CM jobs that you have submitted

Gets a list of protected health information (PHI) detection jobs that you have submitted

Gets a list of InferRxNorm jobs that you have submitted

Starts an asynchronous medical entity detection job for a collection of documents

Starts an asynchronous job to detect medical conditions and link them to the ICD-10-CM ontology

Starts an asynchronous job to detect protected health information (PHI)

Starts an asynchronous job to detect medication entities and link them to the RxNorm ontology

Stops a medical entities detection job in progress

Stops an InferICD10CM inference job in progress

Stops a protected health information (PHI) detection job in progress

Stops an InferRxNorm inference job in progress

Amazon Lex Model Building Service

Creates a new version of the bot based on the $LATEST version

Creates a new version of an intent based on the $LATEST version of the intent

Creates a new version of a slot type based on the $LATEST version of the specified slot type

Deletes all versions of the bot, including the $LATEST version

Deletes an alias for the specified bot

Deletes the association between an Amazon Lex bot and a messaging platform

Deletes a specific version of a bot

Deletes all versions of the intent, including the $LATEST version

Deletes a specific version of an intent

Deletes all versions of the slot type, including the $LATEST version

Deletes a specific version of a slot type

Deletes stored utterances

Returns metadata information for a specific bot

Returns information about an Amazon Lex bot alias

Returns a list of aliases for a specified Amazon Lex bot

Returns information about the association between an Amazon Lex bot and a messaging platform

Returns a list of all of the channels associated with the specified bot

Gets information about all of the versions of a bot

Returns bot information as follows:

Returns information about a built-in intent

Gets a list of built-in intents that meet the specified criteria

Gets a list of built-in slot types that meet the specified criteria

Exports the contents of a Amazon Lex resource in a specified format

Gets information about an import job started with the StartImport operation

Returns information about an intent

Gets information about all of the versions of an intent

Returns intent information as follows:

Returns information about a specific version of a slot type

Gets information about all versions of a slot type

Returns slot type information as follows:

Use the GetUtterancesView operation to get information about the utterances that your users have made to your bot

Gets a list of tags associated with the specified resource

Creates an Amazon Lex conversational bot or replaces an existing bot

Creates an alias for the specified version of the bot or replaces an alias for the specified bot

Creates an intent or replaces an existing intent

Creates a custom slot type or replaces an existing custom slot type

Starts a job to import a resource to Amazon Lex

Adds the specified tags to the specified resource

Removes tags from a bot, bot alias or bot channel

Amazon Lex Runtime Service

Removes session information for a specified bot, alias, and user ID

Returns session information for a specified bot, alias, and user ID

Sends user input (text or speech) to Amazon Lex

Sends user input to Amazon Lex

Creates a new session or modifies an existing session with an Amazon Lex bot

Amazon Machine Learning

Adds one or more tags to an object, up to a limit of 10

Generates predictions for a group of observations

Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS)

Creates a DataSource from a database hosted on an Amazon Redshift cluster

Creates a DataSource object

Creates a new Evaluation of an MLModel

Creates a new MLModel using the DataSource and the recipe as information sources

Creates a real-time endpoint for the MLModel

Assigns the DELETED status to a BatchPrediction, rendering it unusable

Assigns the DELETED status to a DataSource, rendering it unusable

Assigns the DELETED status to an Evaluation, rendering it unusable

Assigns the DELETED status to an MLModel, rendering it unusable

Deletes a real time endpoint of an MLModel

Deletes the specified tags associated with an ML object

Returns a list of BatchPrediction operations that match the search criteria in the request

Returns a list of DataSource that match the search criteria in the request

Returns a list of DescribeEvaluations that match the search criteria in the request

Returns a list of MLModel that match the search criteria in the request

Describes one or more of the tags for your Amazon ML object

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource

Returns an Evaluation that includes metadata as well as the current status of the Evaluation

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel

Generates a prediction for the observation using the specified ML Model

Updates the BatchPredictionName of a BatchPrediction

Updates the DataSourceName of a DataSource

Updates the EvaluationName of an Evaluation

Updates the MLModelName and the ScoreThreshold of an MLModel

Amazon Personalize

Creates a batch inference job

Creates a campaign by deploying a solution version

Creates an empty dataset and adds it to the specified dataset group

Creates an empty dataset group

Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset

Creates an event tracker that you use when sending event data to the specified dataset group using the PutEvents API

Creates a recommendation filter

Creates an Amazon Personalize schema from the specified schema string

Creates the configuration for training a model

Trains or retrains an active solution

Removes a campaign by deleting the solution deployment

Deletes a dataset

Deletes a dataset group

Deletes the event tracker

Deletes a filter

Deletes a schema

Deletes all versions of a solution and the Solution object itself

Describes the given algorithm

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

Describes the given campaign, including its status

Describes the given dataset

Describes the given dataset group

Describes the dataset import job created by CreateDatasetImportJob, including the import job status

Describes an event tracker

Describes the given feature transformation

Describes a filter's properties

Describes a recipe

Describes a schema

Describes a solution

Describes a specific version of a solution

Gets the metrics for the specified solution version

Gets a list of the batch inference jobs that have been performed off of a solution version

Returns a list of campaigns that use the given solution

Returns a list of dataset groups

Returns a list of dataset import jobs that use the given dataset

Returns the list of datasets contained in the given dataset group

Returns the list of event trackers associated with the account

Lists all filters that belong to a given dataset group

Returns a list of available recipes

Returns the list of schemas associated with the account

Returns a list of solution versions for the given solution

Returns a list of solutions that use the given dataset group

Updates a campaign by either deploying a new solution or changing the value of the campaign's minProvisionedTPS parameter

Amazon Personalize Events

Records user interaction event data

Adds one or more items to an Items dataset

Adds one or more users to a Users dataset

Amazon Personalize Runtime

Re-ranks a list of recommended items for the given user

Returns a list of recommended items

Amazon Polly

Deletes the specified pronunciation lexicon stored in an AWS Region

Returns the list of voices that are available for use when requesting speech synthesis

Returns the content of the specified pronunciation lexicon stored in an AWS Region

Retrieves a specific SpeechSynthesisTask object based on its TaskID

Returns a list of pronunciation lexicons stored in an AWS Region

Returns a list of SpeechSynthesisTask objects ordered by their creation date

Stores a pronunciation lexicon in an AWS Region

Allows the creation of an asynchronous synthesis task, by starting a new SpeechSynthesisTask

Synthesizes UTF-8 input, plain text or SSML, to a stream of bytes

Amazon Rekognition

Compares a face in the source input image with each of the 100 largest faces detected in the target input image

Creates a collection in an AWS Region

Creates a new Amazon Rekognition Custom Labels project

Creates a new version of a model and begins training

Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces in a streaming video

Deletes the specified collection

Deletes faces from a collection

Deletes an Amazon Rekognition Custom Labels project

Deletes an Amazon Rekognition Custom Labels model

Deletes the stream processor identified by Name

Describes the specified collection

Lists and describes the models in an Amazon Rekognition Custom Labels project

Lists and gets information about your Amazon Rekognition Custom Labels projects

Provides information about a stream processor created by CreateStreamProcessor

Detects custom labels in a supplied image by using an Amazon Rekognition Custom Labels model

Detects faces within an image that is provided as input

Detects instances of real-world entities within an image (JPEG or PNG) provided as input

Detects unsafe content in a specified JPEG or PNG format image

Detects Personal Protective Equipment (PPE) worn by people detected in an image

Detects text in the input image and converts it into machine-readable text

Gets the name and additional information about a celebrity based on his or her Amazon Rekognition ID

Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition

Gets the unsafe content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration

Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection

Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch

Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection

Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking

Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection

Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection

Detects faces in the input image and adds them to the specified collection

Returns list of collection IDs in your account

Returns metadata for faces in the specified collection

Gets a list of stream processors that you have created with CreateStreamProcessor

Returns an array of celebrities recognized in the input image

For a given input face ID, searches for matching faces in the collection the face belongs to

For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces

Starts asynchronous recognition of celebrities in a stored video

Starts asynchronous detection of unsafe content in a stored video

Starts asynchronous detection of faces in a stored video

Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video

Starts asynchronous detection of labels in a stored video

Starts the asynchronous tracking of a person's path in a stored video

Starts the running of the version of a model

Starts asynchronous detection of segment detection in a stored video

Starts processing a stream processor

Starts asynchronous detection of text in a stored video

Stops a running model

Stops a running stream processor that was created by CreateStreamProcessor

Amazon SageMaker Service

Creates an association between the source and the destination

Adds or overwrites one or more tags for the specified Amazon SageMaker resource

Associates a trial component with a trial

Creates an action

Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace

Creates a running App for the specified UserProfile

Creates a configuration for running a SageMaker image as a KernelGateway app

Creates an artifact

Creates an Autopilot job

Creates a Git repository as a resource in your Amazon SageMaker account

Starts a model compilation job

Creates a context

Creates a definition for a job that monitors data quality and drift

Creates a device fleet

Creates a Domain used by Amazon SageMaker Studio

Starts a SageMaker Edge Manager model packaging job

Creates an endpoint using the endpoint configuration specified in the request

Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models

Creates an SageMaker experiment

Create a new FeatureGroup

Creates a flow definition

Defines the settings you will use for the human review workflow user interface

Starts a hyperparameter tuning job

Creates a custom SageMaker image

Creates a version of the SageMaker image specified by ImageName

Creates a job that uses workers to label the data objects in your input dataset

Creates a model in Amazon SageMaker