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Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.

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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 recommendations-batch.

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Creates a campaign by deploying 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, to maintain a 70% utilization. There's a short time delay while the capacity is increased that might cause loss of transactions. It's recommended to start 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

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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

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Creates an empty dataset group. A dataset group contains related datasets that supply data for training a model. A dataset group can contain at most three datasets, one for each type of dataset:

  • Interactions

  • Items

  • Users

To train a model (create a solution), a dataset group that contains an Interactions dataset is required. Call CreateDataset to add a dataset to the group.

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 AWS Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an AWS Identity and Access Management (IAM) role that has permission to access the key.

APIs that require a dataset group ARN in the request

Related APIs

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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 AWS Identity and Access Management (IAM) role that has permission to read from the data source.

The dataset import job replaces any previous data in the dataset.

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

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Creates an event tracker that you use when sending event data to the specified dataset group using the PutEvents API.

When Amazon Personalize creates an event tracker, it also creates an event-interactions dataset in the dataset group associated with the event tracker. The event-interactions dataset stores the event data from the PutEvents call. The contents of this dataset are not available to the user.

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 send event data you include your tracking ID. The tracking ID identifies the customer and authorizes the customer to send the data.

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

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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. You specify a schema when you call CreateDataset.

Related APIs

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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.

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

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Trains or retrains an active solution. 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 -or- CREATE FAILED

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

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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 more information on campaigns, see CreateCampaign.

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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.

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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.

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

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

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