Module Awso_cleanroomsml_syncSource

The configuration for defining custom patterns to be redacted from logs and error messages. This is for the CUSTOM config under entitiesToRedact. Both CustomEntityConfig and entitiesToRedact need to be present or not present.

The configuration properties for the worker compute environment. These properties allow you to customize the compute settings for your Clean Rooms workloads.

The configuration for log redaction.

Defines who will receive inference results.

Provides information about an Amazon S3 bucket and path.

Provides information about the member who will receive trained model exports.

Defines the Glue data source that contains the training data.

Metadata for a column.

Configuration information about the compute workers that perform the transform job.

The detailed information for a specific budget period, including time boundaries and budget amounts.

Properties that define how a specific data column should be handled during synthetic data generation, including its name, type, and role in predictive modeling.

A score that measures the vulnerability of synthetic data to membership inference attacks and provides both the numerical score and the version of the attack methodology used for evaluation.

Provides the information necessary for a user to access the logs.

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.

Defines the Amazon S3 bucket where the configured audience is stored.

Defines information about the Glue data source that contains the training data.

Provides configuration information for the instances that will perform the compute work.

The parameters for the SQL type Protected Query.

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.

Information about the maximum output size for a trained model inference job.

Provides the configuration policy for metrics generation.

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.

The size of the generated audience. Must match one of the sizes in the configured audience model.

Configuration information about how the inference output is stored.

Configuration information necessary for the configure audience model output.

Details about the status of a resource.

Information about the output of the trained model export job.

Defines the Glue data source and schema mapping information.

Provides information necessary to perform the protected query.

An access budget that defines consumption limits for a specific resource within defined time periods.

Contains classification information for data columns, including mappings that specify how columns should be handled during synthetic data generation and privacy analysis.

Privacy evaluation scores that measure the privacy characteristics of the generated synthetic data, including assessments of potential privacy risks such as membership inference attacks.

Information about the model metric that is reported for a trained model.

Information about how the trained model exports are configured.

Provides configuration information for the trained model inference job.

The configuration policy for the trained models.

The relevance score of a generated audience.

The Amazon S3 location where the exported model artifacts are stored.

Provides information about the training dataset.

Summary information about the trained model.

Provides information about the trained model inference job.

Provides summary information about the ML input channel.

Provides summary information about a configured model algorithm.

Provides summary information about the configured model algorithm association.

Information about the configured audience model.

Provides summary information about a trained model in a collaboration.

Provides summary information about a trained model inference job in a collaboration.

Provides summary information about a trained model export job in a collaboration.

Provides summary information about an ML input channel in a collaboration.

Provides summary information about a configured model algorithm in a collaboration.

Information about the audience model.

Provides information about the configured audience generation job.

Provides information about the audience export job.

Defines where the training dataset is located, what type of data it contains, and how to access the data.

Information about the model training data channel. A training data channel is a named data source that the training algorithms can consume.

Provides the data source that is used to define an input channel.

Parameters that control the generation of synthetic data for custom model training, including privacy settings and column classification details.

Comprehensive evaluation metrics for synthetic data that assess both the utility of the generated data for machine learning tasks and its privacy preservation characteristics.

Information about the privacy configuration policies for a configured model algorithm association.

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.

You do not have sufficient access to perform this action.

You can't complete this action because another resource depends on this resource.

The resource you are requesting does not exist.

The request parameters for this request are incorrect.

Returns the relevance scores at these audience sizes when used in the GetAudienceGenerationJob for a specified audience generation job and configured audience model. Specifies the list of allowed audienceSize values when used in the StartAudienceExportJob for an audience generation job. You can use the ABSOLUTE AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the Percentage AudienceSize to configure sizes in the range 1-100 percent.

You have exceeded your service quota.

The request was denied due to request throttling.

Provides execution parameters for the inference container.

Defines the resources used to perform model inference.

Defines information about the data source used for model inference.

Defines the Amazon S3 bucket where the seed audience for the generating audience is stored.

Configuration information about how the exported model artifacts are stored.

Information about the EC2 resources that are used to train the model.

The criteria used to stop model training.

Provides information about the data source that is used to create an ML input channel.

The privacy budget information that controls access to Clean Rooms ML input channels.

Configuration settings for synthetic data generation, including the parameters that control data synthesis and the evaluation scores that measure the quality and privacy characteristics of the generated synthetic data.

Provides configuration information for the dockerized container where the model algorithm is stored.

Provides configuration information for the inference container.

Information about the privacy configuration for a configured model algorithm association.

Metrics that describe the quality of the generated audience.

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Provides the information necessary to update a configured audience model. Updates that impact audience generation jobs take effect when a new job starts, but do not impact currently running jobs.

Provides the information necessary to update a configured audience model. Updates that impact audience generation jobs take effect when a new job starts, but do not impact currently running jobs.

Removes metadata tags from a specified resource.

Removes metadata tags from a specified resource.

Adds metadata tags to a specified resource.

Adds metadata tags to a specified resource.

Defines the information necessary to begin a trained model inference job.

Defines the information necessary to begin a trained model inference job.

Provides the information necessary to start a trained model export job.

Information necessary to start the audience generation job.

Information necessary to start the audience generation job.

Export an audience of a specified size after you have generated an audience.

Assigns information about an ML configuration.

Create or update the resource policy for a configured audience model.

Create or update the resource policy for a configured audience model.

Returns a list of training datasets.

Returns a list of training datasets.

Returns a list of trained models.

Returns a list of trained models.

Returns a list of trained model versions for a specified trained model. This operation allows you to view all versions of a trained model, including information about their status and creation details. You can use this to track the evolution of your trained models and select specific versions for inference or further training.

Returns a list of trained model versions for a specified trained model. This operation allows you to view all versions of a trained model, including information about their status and creation details. You can use this to track the evolution of your trained models and select specific versions for inference or further training.

Returns a list of trained model inference jobs that match the request parameters.

Returns a list of trained model inference jobs that match the request parameters.

Returns a list of tags for a provided resource.

Returns a list of tags for a provided resource.

Returns a list of ML input channels.

Returns a list of ML input channels.

Returns a list of configured model algorithms.

Returns a list of configured model algorithms.

Returns a list of configured model algorithm associations.

Returns a list of configured model algorithm associations.

Returns a list of the configured audience models.

Returns a list of the configured audience models.

Returns a list of the trained models in a collaboration.

Returns a list of the trained models in a collaboration.

Returns a list of trained model inference jobs in a specified collaboration.

Returns a list of trained model inference jobs in a specified collaboration.

Returns a list of the export jobs for a trained model in a collaboration.

Returns a list of the export jobs for a trained model in a collaboration.

Returns a list of the ML input channels in a collaboration.

Returns a list of the ML input channels in a collaboration.

Returns a list of the configured model algorithm associations in a collaboration.

Returns a list of the configured model algorithm associations in a collaboration.

Returns a list of audience models.

Returns a list of audience models.

Returns a list of audience generation jobs.

Returns a list of audience generation jobs.

Returns a list of the audience export jobs.

Returns a list of the audience export jobs.

Returns information about a training dataset.

Returns information about a training dataset.

Returns information about a trained model.

Returns information about a trained model.

Returns information about a trained model inference job.

Returns information about a trained model inference job.

Returns information about an ML input channel.

Returns information about an ML input channel.

Returns information about a specific ML configuration.

Returns information about a specific ML configuration.

Returns information about a configured model algorithm.

Returns information about a configured model algorithm.

Returns information about a configured model algorithm association.

Returns information about a configured model algorithm association.

Returns information about a specified configured audience model.

Returns information about a specified configured audience model.

Returns information about a configured audience model policy.

Returns information about a configured audience model policy.

Returns information about a trained model in a collaboration.

Returns information about a trained model in a collaboration.

Returns information about a specific ML input channel in a collaboration.

Returns information about a specific ML input channel in a collaboration.

Returns information about the configured model algorithm association in a collaboration.

Returns information about the configured model algorithm association in a collaboration.

Returns information about an audience model

Returns information about an audience model

Returns information about an audience generation job.

Returns information about an audience generation job.

Specifies a training dataset that you want to delete. You can't delete a training dataset if there are any audience models that depend on the training dataset. In Clean Rooms ML, the TrainingDataset is metadata that points to a Glue table, which is read only during AudienceModel creation. This action deletes the metadata.

Deletes the model artifacts stored by the service.

Provides the information necessary to delete an ML input channel.

Deletes a ML modeling configuration.

Deletes a configured model algorithm.

Deletes a configured model algorithm association.

Deletes the specified configured audience model. You can't delete a configured audience model if there are any lookalike models that use the configured audience model. If you delete a configured audience model, it will be removed from any collaborations that it is associated to.

Deletes the specified configured audience model policy.

Specifies an audience model that you want to delete. You can't delete an audience model if there are any configured audience models that depend on the audience model.

Deletes the specified audience generation job, and removes all data associated with the job.

Defines the information necessary to create a training dataset. In Clean Rooms ML, the TrainingDataset is metadata that points to a Glue table, which is read only during AudienceModel creation.

Defines the information necessary to create a training dataset. In Clean Rooms ML, the TrainingDataset is metadata that points to a Glue table, which is read only during AudienceModel creation.

Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.

Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.

Provides the information to create an ML input channel. An ML input channel is the result of a query that can be used for ML modeling.

Provides the information to create an ML input channel. An ML input channel is the result of a query that can be used for ML modeling.

Creates a configured model algorithm using a container image stored in an ECR repository.

Creates a configured model algorithm using a container image stored in an ECR repository.

Associates a configured model algorithm to a collaboration for use by any member of the collaboration.

Associates a configured model algorithm to a collaboration for use by any member of the collaboration.

Defines the information necessary to create a configured audience model.

Defines the information necessary to create a configured audience model.

Defines the information necessary to create an audience model. An audience model is a machine learning model that Clean Rooms ML trains to measure similarity between users. Clean Rooms ML manages training and storing the audience model. The audience model can be used in multiple calls to the StartAudienceGenerationJob API.

Defines the information necessary to create an audience model. An audience model is a machine learning model that Clean Rooms ML trains to measure similarity between users. Clean Rooms ML manages training and storing the audience model. The audience model can be used in multiple calls to the StartAudienceGenerationJob API.

Submits a request to cancel the trained model job.

Submits a request to cancel a trained model inference job.