Module types 

Data structures used by operation inputs/outputs.

Modules

builders

Builders

error

Error types that Amazon Personalize can respond with.

Structs

Algorithm

Describes a custom algorithm.

AlgorithmImage

Describes an algorithm image.

AutoMlConfig

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.

AutoMlResult

When the solution performs AutoML (performAutoML is true in CreateSolution), specifies the recipe that best optimized the specified metric.

AutoTrainingConfig

The automatic training configuration to use when performAutoTraining is true.

BatchInferenceJob

Contains information on a batch inference job.

BatchInferenceJobConfig

The configuration details of a batch inference job.

BatchInferenceJobInput

The input configuration of a batch inference job.

BatchInferenceJobOutput

The output configuration parameters of a batch inference job.

BatchInferenceJobSummary

A truncated version of the BatchInferenceJob. The ListBatchInferenceJobs operation returns a list of batch inference job summaries.

BatchSegmentJob

Contains information on a batch segment job.

BatchSegmentJobInput

The input configuration of a batch segment job.

BatchSegmentJobOutput

The output configuration parameters of a batch segment job.

BatchSegmentJobSummary

A truncated version of the BatchSegmentJob datatype. ListBatchSegmentJobs operation returns a list of batch segment job summaries.

Campaign

An object that describes the deployment of a solution version. For more information on campaigns, see CreateCampaign.

CampaignConfig

The configuration details of a campaign.

CampaignSummary

Provides a summary of the properties of a campaign. For a complete listing, call the DescribeCampaign API.

CampaignUpdateSummary

Provides a summary of the properties of a campaign update. For a complete listing, call the DescribeCampaign API.

CategoricalHyperParameterRange

Provides the name and range of a categorical hyperparameter.

ContinuousHyperParameterRange

Provides the name and range of a continuous hyperparameter.

DataDeletionJob

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.

DataDeletionJobSummary

Provides a summary of the properties of a data deletion job. For a complete listing, call the DescribeDataDeletionJob API operation.

DataSource

Describes the data source that contains the data to upload to a dataset, or the list of records to delete from Amazon Personalize.

Dataset

Provides metadata for a dataset.

DatasetExportJob

Describes a job that exports a dataset to an Amazon S3 bucket. For more information, see CreateDatasetExportJob.

A dataset export job can be in one of the following states:

• CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

DatasetExportJobOutput

The output configuration parameters of a dataset export job.

DatasetExportJobSummary

Provides a summary of the properties of a dataset export job. For a complete listing, call the DescribeDatasetExportJob API.

DatasetGroup

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.

You can specify an Key Management Service (KMS) key to encrypt the datasets in the group.

DatasetGroupSummary

Provides a summary of the properties of a dataset group. For a complete listing, call the DescribeDatasetGroup API.

DatasetImportJob

Describes a job that imports training data from a data source (Amazon S3 bucket) to an Amazon Personalize dataset. For more information, see CreateDatasetImportJob.

A dataset import job can be in one of the following states:

• CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

DatasetImportJobSummary

Provides a summary of the properties of a dataset import job. For a complete listing, call the DescribeDatasetImportJob API.

DatasetSchema

Describes the schema for a dataset. For more information on schemas, see CreateSchema.

DatasetSchemaSummary

Provides a summary of the properties of a dataset schema. For a complete listing, call the DescribeSchema API.

DatasetSummary

Provides a summary of the properties of a dataset. For a complete listing, call the DescribeDataset API.

DatasetUpdateSummary

Describes an update to a dataset.

DefaultCategoricalHyperParameterRange

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).

DefaultContinuousHyperParameterRange

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).

DefaultHyperParameterRanges

Specifies the hyperparameters and their default ranges. Hyperparameters can be categorical, continuous, or integer-valued.

DefaultIntegerHyperParameterRange

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).

EventParameters

Describes the parameters of events, which are used in solution creation.

EventTracker

Provides information about an event tracker.

EventTrackerSummary

Provides a summary of the properties of an event tracker. For a complete listing, call the DescribeEventTracker API.

EventsConfig

Describes the configuration of events, which are used in solution creation.

FeatureTransformation

Provides feature transformation information. Feature transformation is the process of modifying raw input data into a form more suitable for model training.

FieldsForThemeGeneration

A string to string map of the configuration details for theme generation.

Filter

Contains information on a recommendation filter, including its ARN, status, and filter expression.

FilterSummary

A short summary of a filter's attributes.

HpoConfig

Describes the properties for hyperparameter optimization (HPO).

HpoObjective

The metric to optimize during hyperparameter optimization (HPO).

Amazon Personalize doesn't support configuring the hpoObjective at this time.

HpoResourceConfig

Describes the resource configuration for hyperparameter optimization (HPO).

HyperParameterRanges

Specifies the hyperparameters and their ranges. Hyperparameters can be categorical, continuous, or integer-valued.

IntegerHyperParameterRange

Provides the name and range of an integer-valued hyperparameter.

MetricAttribute

Contains information on a metric that a metric attribution reports on. For more information, see Measuring impact of recommendations.

MetricAttribution

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.

MetricAttributionOutput

The output configuration details for a metric attribution.

MetricAttributionSummary

Provides a summary of the properties of a metric attribution. For a complete listing, call the DescribeMetricAttribution.

OptimizationObjective

Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.

Recipe

Provides information about a recipe. Each recipe provides an algorithm that Amazon Personalize uses in model training when you use the CreateSolution operation.

RecipeSummary

Provides a summary of the properties of a recipe. For a complete listing, call the DescribeRecipe API.

Recommender

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.

RecommenderConfig

The configuration details of the recommender.

RecommenderSummary

Provides a summary of the properties of the recommender.

RecommenderUpdateSummary

Provides a summary of the properties of a recommender update. For a complete listing, call the DescribeRecommender API.

S3DataConfig

The configuration details of an Amazon S3 input or output bucket.

Solution

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.

An object that provides information about a solution. A solution includes the custom recipe, customized parameters, and trained models (Solution Versions) that Amazon Personalize uses to generate recommendations.

After you create a solution, you can’t change its configuration. If you need to make changes, you can clone the solution with the Amazon Personalize console or create a new one.

SolutionConfig

Describes the configuration properties for the solution.

SolutionSummary

Provides a summary of the properties of a solution. For a complete listing, call the DescribeSolution API.

SolutionUpdateConfig

The configuration details of the solution update.

SolutionUpdateSummary

Provides a summary of the properties of a solution update. For a complete listing, call the DescribeSolution API.

SolutionVersion

An object that provides information about a specific version of a Solution in a Custom dataset group.

SolutionVersionSummary

Provides a summary of the properties of a solution version. For a complete listing, call the DescribeSolutionVersion API.

Tag

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.

ThemeGenerationConfig

The configuration details for generating themes with a batch inference job.

TrainingDataConfig

The training data configuration to use when creating a domain recommender or custom solution version (trained model).

TunedHpoParams

If hyperparameter optimization (HPO) was performed, contains the hyperparameter values of the best performing model.

Enums

BatchInferenceJobMode

When writing a match expression against BatchInferenceJobMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

Domain

When writing a match expression against Domain, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

ImportMode

When writing a match expression against ImportMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

IngestionMode

When writing a match expression against IngestionMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

ObjectiveSensitivity

When writing a match expression against ObjectiveSensitivity, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

RecipeProvider

When writing a match expression against RecipeProvider, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

TrainingMode

When writing a match expression against TrainingMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

TrainingType

When writing a match expression against TrainingType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.