AWS – Amazon Personalize simplifies implementation by extending column limits

Amazon Personalize now makes it easier to implement machine learning powered personalization by reducing the need for experimentation with increased dataset column limits. Amazon Personalize uses datasets provided by customers to train custom personalization models on their behalf. Some customers experiment with multiple iterations of their datasets in order to optimize model performance while fitting within column limits on datasets. With this launch, we increased column limits to reduce the need for experimentation and accelerate implementation. Customers can now bring double the number of columns to their Items datasets (100 columns) and five times as many columns to their Users datasets (25 columns). With these increases, customers can now bring more of their data and allow Personalize to optimize model performance on their behalf.

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