Package-level declarations
Types
Describes an algorithm image.
When the solution performs AutoML (performAutoML is true in CreateSolution), Amazon Personalize determines which recipe, from the specified list, optimizes the given metric. Amazon Personalize then uses that recipe for the solution.
When the solution performs AutoML (performAutoML is true in CreateSolution), specifies the recipe that best optimized the specified metric.
The automatic training configuration to use when performAutoTraining is true.
Contains information on a batch inference job.
The configuration details of a batch inference job.
The input configuration of a batch inference job.
The output configuration parameters of a batch inference job.
A truncated version of the BatchInferenceJob. The ListBatchInferenceJobs operation returns a list of batch inference job summaries.
Contains information on a batch segment job.
The input configuration of a batch segment job.
The output configuration parameters of a batch segment job.
A truncated version of the BatchSegmentJob datatype. ListBatchSegmentJobs operation returns a list of batch segment job summaries.
An object that describes the deployment of a solution version. For more information on campaigns, see CreateCampaign.
The configuration details of a campaign.
Provides a summary of the properties of a campaign. For a complete listing, call the DescribeCampaign API.
Provides a summary of the properties of a campaign update. For a complete listing, call the DescribeCampaign API.
Provides the name and range of a categorical hyperparameter.
Provides the name and range of a continuous hyperparameter.
Describes a job that deletes all references to specific users from an Amazon Personalize dataset group in batches. For information about creating a data deletion job, see Deleting users.
Provides a summary of the properties of a data deletion job. For a complete listing, call the DescribeDataDeletionJob API operation.
Describes a job that exports a dataset to an Amazon S3 bucket. For more information, see CreateDatasetExportJob.
The output configuration parameters of a dataset export job.
Provides a summary of the properties of a dataset export job. For a complete listing, call the DescribeDatasetExportJob API.
A dataset group is a collection of related datasets (Item interactions, Users, Items, Actions, Action interactions). You create a dataset group by calling CreateDatasetGroup. You then create a dataset and add it to a dataset group by calling CreateDataset. The dataset group is used to create and train a solution by calling CreateSolution. A dataset group can contain only one of each type of dataset.
Provides a summary of the properties of a dataset group. For a complete listing, call the DescribeDatasetGroup API.
Describes a job that imports training data from a data source (Amazon S3 bucket) to an Amazon Personalize dataset. For more information, see CreateDatasetImportJob.
Provides a summary of the properties of a dataset import job. For a complete listing, call the DescribeDatasetImportJob API.
Describes the schema for a dataset. For more information on schemas, see CreateSchema.
Provides a summary of the properties of a dataset schema. For a complete listing, call the DescribeSchema API.
Provides a summary of the properties of a dataset. For a complete listing, call the DescribeDataset API.
Describes an update to a dataset.
Describes the data source that contains the data to upload to a dataset, or the list of records to delete from Amazon Personalize.
Provides the name and default range of a categorical hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
Provides the name and default range of a continuous hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
Specifies the hyperparameters and their default ranges. Hyperparameters can be categorical, continuous, or integer-valued.
Provides the name and default range of a integer-valued hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
Describes the parameters of events, which are used in solution creation.
Describes the configuration of events, which are used in solution creation.
Provides information about an event tracker.
Provides a summary of the properties of an event tracker. For a complete listing, call the DescribeEventTracker API.
Provides feature transformation information. Feature transformation is the process of modifying raw input data into a form more suitable for model training.
A string to string map of the configuration details for theme generation.
A short summary of a filter's attributes.
The metric to optimize during hyperparameter optimization (HPO).
Describes the resource configuration for hyperparameter optimization (HPO).
Specifies the hyperparameters and their ranges. Hyperparameters can be categorical, continuous, or integer-valued.
Provides the name and range of an integer-valued hyperparameter.
Provide a valid value for the field or parameter.
The token is not valid.
The limit on the number of requests per second has been exceeded.
Contains information on a metric that a metric attribution reports on. For more information, see Measuring impact of recommendations.
Contains information on a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you import the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations.
The output configuration details for a metric attribution.
Provides a summary of the properties of a metric attribution. For a complete listing, call the DescribeMetricAttribution.
Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.
Base class for all service related exceptions thrown by the Personalize client
Provides information about a recipe. Each recipe provides an algorithm that Amazon Personalize uses in model training when you use the CreateSolution operation.
Provides a summary of the properties of a recipe. For a complete listing, call the DescribeRecipe API.
Describes a recommendation generator for a Domain dataset group. You create a recommender in a Domain dataset group for a specific domain use case (domain recipe), and specify the recommender in a GetRecommendations request.
The configuration details of the recommender.
Provides a summary of the properties of the recommender.
Provides a summary of the properties of a recommender update. For a complete listing, call the DescribeRecommender API.
The specified resource already exists.
The specified resource is in use.
Could not find the specified resource.
The configuration details of an Amazon S3 input or output bucket.
By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing.
Describes the configuration properties for the solution.
Provides a summary of the properties of a solution. For a complete listing, call the DescribeSolution API.
The configuration details of the solution update.
Provides a summary of the properties of a solution update. For a complete listing, call the DescribeSolution API.
An object that provides information about a specific version of a Solution in a Custom dataset group.
Provides a summary of the properties of a solution version. For a complete listing, call the DescribeSolutionVersion API.
The optional metadata that you apply to resources to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define. For more information see Tagging Amazon Personalize resources.
The configuration details for generating themes with a batch inference job.
The request contains more tag keys than can be associated with a resource (50 tag keys per resource).
You have exceeded the maximum number of tags you can apply to this resource.
The training data configuration to use when creating a domain recommender or custom solution version (trained model).
If hyperparameter optimization (HPO) was performed, contains the hyperparameter values of the best performing model.