Amazon Personalize

Creates a batch inference job. The operation can handle up to 50 million records and the input file must be in JSON format. For more information, see Creating a batch inference job.

Creates a batch segment job. The operation can handle up to 50 million records and the input file must be in JSON format. For more information, see Getting batch recommendations and user segments.

Creates a campaign that deploys a solution version. When a client calls the GetRecommendations and GetPersonalizedRanking APIs, a campaign is specified in the request. Minimum Provisioned TPS and Auto-Scaling A transaction is a single GetRecommendations or GetPersonalizedRanking call. Transactions per second (TPS) is the throughput and unit of billing for Amazon Personalize. The minimum provisioned TPS (minProvisionedTPS) specifies the baseline throughput provisioned by Amazon Personalize, and thus, the minimum billing charge. If your TPS increases beyond minProvisionedTPS, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minProvisionedTPS. There's a short time delay while the capacity is increased that might cause loss of transactions. The actual TPS used is calculated as the average requests/second within a 5-minute window. You pay for maximum of either the minimum provisioned TPS or the actual TPS. We recommend starting with a low minProvisionedTPS, track your usage using Amazon CloudWatch metrics, and then increase the minProvisionedTPS as necessary. Status A campaign can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the campaign status, call DescribeCampaign. Wait until the status of the campaign is ACTIVE before asking the campaign for recommendations. Related APIs ListCampaigns DescribeCampaign UpdateCampaign DeleteCampaign

Creates an empty dataset and adds it to the specified dataset group. Use CreateDatasetImportJob to import your training data to a dataset. There are three types of datasets: Interactions Items Users Each dataset type has an associated schema with required field types. Only the Interactions dataset is required in order to train a model (also referred to as creating a solution). A dataset can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the dataset, call DescribeDataset. Related APIs CreateDatasetGroup ListDatasets DescribeDataset DeleteDataset

Creates a job that exports data from your dataset to an Amazon S3 bucket. To allow Amazon Personalize to export the training data, you must specify an service-linked IAM role that gives Amazon Personalize PutObject permissions for your Amazon S3 bucket. For information, see Exporting a dataset in the Amazon Personalize developer guide. Status A dataset export job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of the export job, call DescribeDatasetExportJob, and specify the Amazon Resource Name (ARN) of the dataset export job. The dataset export is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.

Creates an empty dataset group. A dataset group is a container for Amazon Personalize resources. A dataset group can contain at most three datasets, one for each type of dataset: Interactions Items Users A dataset group can be a Domain dataset group, where you specify a domain and use pre-configured resources like recommenders, or a Custom dataset group, where you use custom resources, such as a solution with a solution version, that you deploy with a campaign. If you start with a Domain dataset group, you can still add custom resources such as solutions and solution versions trained with recipes for custom use cases and deployed with campaigns. A dataset group can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING To get the status of the dataset group, call DescribeDatasetGroup. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the creation failed. You must wait until the status of the dataset group is ACTIVE before adding a dataset to the group. You can specify an Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an Identity and Access Management (IAM) role that has permission to access the key. APIs that require a dataset group ARN in the request CreateDataset CreateEventTracker CreateSolution Related APIs ListDatasetGroups DescribeDatasetGroup DeleteDatasetGroup

Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset. To allow Amazon Personalize to import the training data, you must specify an IAM service role that has permission to read from the data source, as Amazon Personalize makes a copy of your data and processes it internally. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources. By default, a dataset import job replaces any existing data in the dataset that you imported in bulk. To add new records without replacing existing data, specify INCREMENTAL for the import mode in the CreateDatasetImportJob operation. Status A dataset import job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of the import job, call DescribeDatasetImportJob, providing the Amazon Resource Name (ARN) of the dataset import job. The dataset import is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed. Importing takes time. You must wait until the status shows as ACTIVE before training a model using the dataset. Related APIs ListDatasetImportJobs DescribeDatasetImportJob

Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API. Only one event tracker can be associated with a dataset group. You will get an error if you call CreateEventTracker using the same dataset group as an existing event tracker. When you create an event tracker, the response includes a tracking ID, which you pass as a parameter when you use the PutEvents operation. Amazon Personalize then appends the event data to the Interactions dataset of the dataset group you specify in your event tracker. The event tracker can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the event tracker, call DescribeEventTracker. The event tracker must be in the ACTIVE state before using the tracking ID. Related APIs ListEventTrackers DescribeEventTracker DeleteEventTracker

Creates a recommendation filter. For more information, see Filtering recommendations and user segments.

Creates a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you imported the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations.

Creates a recommender with the recipe (a Domain dataset group use case) you specify. You create recommenders for a Domain dataset group and specify the recommender's Amazon Resource Name (ARN) when you make a GetRecommendations request. Minimum recommendation requests per second When you create a recommender, you can configure the recommender's minimum recommendation requests per second. The minimum recommendation requests per second (minRecommendationRequestsPerSecond) specifies the baseline recommendation request throughput provisioned by Amazon Personalize. The default minRecommendationRequestsPerSecond is 1. A recommendation request is a single GetRecommendations operation. Request throughput is measured in requests per second and Amazon Personalize uses your requests per second to derive your requests per hour and the price of your recommender usage. If your requests per second increases beyond minRecommendationRequestsPerSecond, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minRecommendationRequestsPerSecond. There's a short time delay while the capacity is increased that might cause loss of requests. Your bill is the greater of either the minimum requests per hour (based on minRecommendationRequestsPerSecond) or the actual number of requests. The actual request throughput used is calculated as the average requests/second within a one-hour window. We recommend starting with the default minRecommendationRequestsPerSecond, track your usage using Amazon CloudWatch metrics, and then increase the minRecommendationRequestsPerSecond as necessary. Status A recommender can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE DELETE PENDING > DELETE IN_PROGRESS To get the recommender status, call DescribeRecommender. Wait until the status of the recommender is ACTIVE before asking the recommender for recommendations. Related APIs ListRecommenders DescribeRecommender UpdateRecommender DeleteRecommender

Creates an Amazon Personalize schema from the specified schema string. The schema you create must be in Avro JSON format. Amazon Personalize recognizes three schema variants. Each schema is associated with a dataset type and has a set of required field and keywords. If you are creating a schema for a dataset in a Domain dataset group, you provide the domain of the Domain dataset group. You specify a schema when you call CreateDataset. Related APIs ListSchemas DescribeSchema DeleteSchema

Creates the configuration for training a model. A trained model is known as a solution. After the configuration is created, you train the model (create a solution) by calling the CreateSolutionVersion operation. Every time you call CreateSolutionVersion, a new version of the solution is created. After creating a solution version, you check its accuracy by calling GetSolutionMetrics. When you are satisfied with the version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API. To train a model, Amazon Personalize requires training data and a recipe. The training data comes from the dataset group that you provide in the request. A recipe specifies the training algorithm and a feature transformation. You can specify one of the predefined recipes provided by Amazon Personalize. Alternatively, you can specify performAutoML and Amazon Personalize will analyze your data and select the optimum USER_PERSONALIZATION recipe for you. Amazon Personalize doesn't support configuring the hpoObjective for solution hyperparameter optimization at this time. Status A solution can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the solution, call DescribeSolution. Wait until the status shows as ACTIVE before calling CreateSolutionVersion. Related APIs ListSolutions CreateSolutionVersion DescribeSolution DeleteSolution ListSolutionVersions DescribeSolutionVersion

Trains or retrains an active solution in a Custom dataset group. A solution is created using the CreateSolution operation and must be in the ACTIVE state before calling CreateSolutionVersion. A new version of the solution is created every time you call this operation. Status A solution version can be in one of the following states: CREATE PENDING CREATE IN_PROGRESS ACTIVE CREATE FAILED CREATE STOPPING CREATE STOPPED To get the status of the version, call DescribeSolutionVersion. Wait until the status shows as ACTIVE before calling CreateCampaign. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed. Related APIs ListSolutionVersions DescribeSolutionVersion ListSolutions CreateSolution DescribeSolution DeleteSolution

Removes a campaign by deleting the solution deployment. The solution that the campaign is based on is not deleted and can be redeployed when needed. A deleted campaign can no longer be specified in a GetRecommendations request. For information on creating campaigns, see CreateCampaign.

Deletes a dataset. You can't delete a dataset if an associated DatasetImportJob or SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on datasets, see CreateDataset.

Deletes a dataset group. Before you delete a dataset group, you must delete the following: All associated event trackers. All associated solutions. All datasets in the dataset group.

Deletes the event tracker. Does not delete the event-interactions dataset from the associated dataset group. For more information on event trackers, see CreateEventTracker.

Deletes a filter.

Deletes a metric attribution.

Deactivates and removes a recommender. A deleted recommender can no longer be specified in a GetRecommendations request.

Deletes a schema. Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, see CreateSchema.

Deletes all versions of a solution and the Solution object itself. Before deleting a solution, you must delete all campaigns based on the solution. To determine what campaigns are using the solution, call ListCampaigns and supply the Amazon Resource Name (ARN) of the solution. You can't delete a solution if an associated SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on solutions, see CreateSolution.

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.

Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments.

Describes the given campaign, including its status. A campaign can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS When the status is CREATE FAILED, the response includes the failureReason key, which describes why. For more information on campaigns, see CreateCampaign.

Describes the given dataset. For more information on datasets, see CreateDataset.

Describes the dataset export job created by CreateDatasetExportJob, including the export job status.

Describes the given dataset group. For more information on dataset groups, see CreateDatasetGroup.

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

Describes an event tracker. The response includes the trackingId and status of the event tracker. For more information on event trackers, see CreateEventTracker.

Describes the given feature transformation.

Describes a filter's properties.

Describes a metric attribution.

Describes a recipe. A recipe contains three items: An algorithm that trains a model. Hyperparameters that govern the training. Feature transformation information for modifying the input data before training. Amazon Personalize provides a set of predefined recipes. You specify a recipe when you create a solution with the CreateSolution API. CreateSolution trains a model by using the algorithm in the specified recipe and a training dataset. The solution, when deployed as a campaign, can provide recommendations using the GetRecommendations API.

Describes the given recommender, including its status. A recommender can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE DELETE PENDING > DELETE IN_PROGRESS When the status is CREATE FAILED, the response includes the failureReason key, which describes why. The modelMetrics key is null when the recommender is being created or deleted. For more information on recommenders, see CreateRecommender.

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

Describes a solution. For more information on solutions, see CreateSolution.

Describes a specific version of a solution. For more information on solutions, see CreateSolution

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.

Gets a list of the batch segment jobs that have been performed off of a solution version that you specify.

Returns a list of campaigns that use the given solution. When a solution is not specified, all the campaigns associated with the account are listed. The response provides the properties for each campaign, including the Amazon Resource Name (ARN). For more information on campaigns, see CreateCampaign.

Returns a list of dataset export jobs that use the given dataset. When a dataset is not specified, all the dataset export jobs associated with the account are listed. The response provides the properties for each dataset export job, including the Amazon Resource Name (ARN). For more information on dataset export jobs, see CreateDatasetExportJob. For more information on datasets, see CreateDataset.

Returns a list of dataset groups. The response provides the properties for each dataset group, including the Amazon Resource Name (ARN). For more information on dataset groups, see CreateDatasetGroup.

Returns a list of dataset import jobs that use the given dataset. When a dataset is not specified, all the dataset import jobs associated with the account are listed. The response provides the properties for each dataset import job, including the Amazon Resource Name (ARN). For more information on dataset import jobs, see CreateDatasetImportJob. For more information on datasets, see CreateDataset.

Returns the list of datasets contained in the given dataset group. The response provides the properties for each dataset, including the Amazon Resource Name (ARN). For more information on datasets, see CreateDataset.

Returns the list of event trackers associated with the account. The response provides the properties for each event tracker, including the Amazon Resource Name (ARN) and tracking ID. For more information on event trackers, see CreateEventTracker.

Lists all filters that belong to a given dataset group.