Importing training data into Amazon Personalize datasets
After you complete create a schema and a dataset, you are ready to import your training data into the dataset. When you import data, you can choose to import records in bulk, individually, or both.
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Bulk imports involve importing a large number of historical records at once. You can prepare bulk data yourself, and import it directly into Amazon Personalize from a CSV file in Amazon S3. For information about how to prepare your data, see Preparing training data for Amazon Personalize. If you need help preparing your data, you can use SageMaker Data Wrangler to prepare and import your bulk item interaction, user, and item data. For more information, see Preparing and importing bulk data using Amazon SageMaker Data Wrangler.
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If you don't have bulk data, you can use individual import operations to collect data and stream events until you meet Amazon Personalize training requirements and the data requirements of your domain use case or recipe. For information about recording events, see Recording real-time events to influence recommendations. For information about importing individual records, see Importing individual records into an Amazon Personalize dataset.
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.
As your catalog grows, update your historical data with additional bulk, or individual data, import operations. For real-time recommendations, keep your Item interactions dataset up to date with your users' behavior. You do this by recording real-time interaction events with an event tracker and the PutEvents operation. For more information, see Recording real-time events to influence recommendations
After you import your data, you are ready to 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. For more information, see Domain recommenders in Amazon Personalize or Custom resources for training and deploying Amazon Personalize models.
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