Package-level declarations
Types
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Defines the Amazon S3 bucket where the configured audience is stored.
Provides information about the audience export job.
Defines the Amazon S3 bucket where the seed audience for the generating audience is stored.
Provides information about the configured audience generation job.
Information about the audience model.
Metrics that describe the quality of the generated audience.
The size of the generated audience. Must match one of the sizes in the configured audience model.
Returns the relevance scores at these audience sizes when used in the GetAudienceGenerationJob for a specified audience generation job and configured audience model.
Base class for all service related exceptions thrown by the CleanRoomsMl client
Provides summary information about a configured model algorithm in a collaboration.
Provides summary information about an ML input channel in a collaboration.
Provides summary information about a trained model export job in a collaboration.
Provides summary information about a trained model inference job in a collaboration.
Provides summary information about a trained model in a collaboration.
Metadata for a column.
Provides configuration information for the instances that will perform the compute work.
Configuration information necessary for the configure audience model output.
Information about the configured audience model.
Provides summary information about the configured model algorithm association.
Provides summary information about a configured model algorithm.
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Provides configuration information for the dockerized container where the model algorithm is stored.
Defines the Glue data source and schema mapping information.
Defines information about the Glue data source that contains the training data.
The Amazon S3 location where the exported model artifacts are stored.
Defines the Glue data source that contains the training data.
Defines an incremental training data channel that references a previously trained model. Incremental training allows you to update an existing trained model with new data, building upon the knowledge from a base model rather than training from scratch. This can significantly reduce training time and computational costs while improving model performance with additional data.
Contains information about an incremental training data channel that was used to create a trained model. This structure provides details about the base model and channel configuration used during incremental training.
Provides configuration information for the inference container.
Provides execution parameters for the inference container.
Configuration information about how the inference output is stored.
Defines who will receive inference results.
Defines the resources used to perform model inference.
Provides information about the data source that is used to create an ML input channel.
Provides the data source that is used to define an input channel.
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Provides the information necessary for a user to access the logs.
Information about the model metric that is reported for a trained model.
Provides the configuration policy for metrics generation.
Provides summary information about the ML input channel.
Configuration information about how the exported model artifacts are stored.
Defines information about the data source used for model inference.
Information about the model training data channel. A training data channel is a named data source that the training algorithms can consume.
Information about the privacy configuration for a configured model algorithm association.
Information about the privacy configuration policies for a configured model algorithm association.
Provides information necessary to perform the protected query.
The parameters for the SQL type Protected Query.
The relevance score of a generated audience.
Information about the EC2 resources that are used to train the model.
The resource you are requesting does not exist.
File format of the returned data.
Provides information about an Amazon S3 bucket and path.
You have exceeded your service quota.
Details about the status of a resource.
The criteria used to stop model training.
The request was denied due to request throttling.
Specifies the maximum size limit for trained model artifacts. This configuration helps control storage costs and ensures that trained models don't exceed specified size constraints. The size limit applies to the total size of all artifacts produced by the training job.
Information about the output of the trained model export job.
Provides information about the member who will receive trained model exports.
Information about how the trained model exports are configured.
The maximum size of the trained model metrics that can be exported. If the trained model metrics dataset is larger than this value, it will not be exported.
Provides configuration information for the trained model inference job.
Provides information about the trained model inference job.
Information about the maximum output size for a trained model inference job.
The configuration policy for the trained models.
Summary information about the trained model.
Provides information about the training dataset.
The request parameters for this request are incorrect.
Configuration information about the compute workers that perform the transform job.