Amazon Personalize workflow - Amazon Personalize
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Amazon Personalize workflow

After you review the Readiness checklist, you are ready to start completing the Amazon Personalize workflow:

  1. Create a dataset group

    A dataset group is a container for Amazon Personalize resources. The type of dataset group you create determines the resources you can create in step 3 of the Amazon Personalize workflow.

    • With a Domain dataset group, you can create recommenders configured for VIDEO_ON_DEMAND or ECOMMERCE domain use cases. You use the recommenders to get recommendations. Amazon Personalize manages their configuration, training, and updates. If you start with a Domain dataset group, you can still add custom resources. You can't create next best action resources, including Actions and Action Interactions datasets, in a domain dataset group.

    • With a Custom dataset group, you can create only custom resources. These including solutions, solution versions, and campaigns. For these resources, you have more control over configurations, updates, and retraining.

  2. Prepare and import data

    You import item interaction, action interaction, item, user, and action records into datasets (Amazon Personalize containers for data). You can import records in bulk or individually. When you import bulk data, you can use Amazon SageMaker Data Wrangler to import data from 40+ sources and prepare it for Amazon Personalize. For more information, see Preparing and importing data using Amazon SageMaker Data Wrangler.

    After you import data into an Amazon Personalize dataset, you can analyze it, export it to an Amazon S3 bucket, update it, or delete it by deleting the dataset. For more information, see Managing data.

  3. Create domain recommenders or custom resources

    After you import your data, create domain recommenders (for Domain dataset groups) or custom resources (for Custom dataset group) to train a model on your data. You use these resources to generate recommendations.

  4. Get recommendations

    Use your recommender or custom campaign to get recommendations. With a Custom dataset group, you can also get batch recommendations or user segments.

After you complete the Amazon Personalize workflow the first time, keep data current, and regularly re-train any custom solutions. This allows your model to learn from your user’s most recent activity and sustains and improves the relevance of recommendations. For more information, see Maintaining recommendation relevance.