Amazon Personalize now makes it easier to add columns to existing datasets
Amazon Personalize now makes it easier to modify datasets by allowing customers to add columns to an existing schema. Amazon Personalize uses datasets provided by customers to train custom personalization models on their behalf. Customers modify existing datasets to add new filtering columns for enhanced business logic and to add new columns that can improve model training. Previously, to add new columns customers would need to reproduce existing resources starting at the dataset level. With this feature, customers can quickly update their schema to append an additional column without having to reproduce resources.
Amazon Personalize now makes it easier to modify datasets by allowing customers to add columns to an existing schema. Amazon Personalize uses datasets provided by customers to train custom personalization models on their behalf. Customers modify existing datasets to add new filtering columns for enhanced business logic and to add new columns that can improve model training. Previously, to add new columns customers would need to reproduce existing resources starting at the dataset level. With this feature, customers can quickly update their schema to append an additional column without having to reproduce resources.
To add a column to an existing dataset, simply select your dataset in the Personalize console and click “Replace Schema.” Add the new columns and import new data. The new columns will then be available for filtering. To use the new columns for training, simply clone your existing Solution, select the new columns for training, and train a new Solution version.
Amazon Personalize enables you to personalize your website, app, ads, emails, and more, using the same machine learning technology used by Amazon, without requiring any prior machine learning experience. To get started with Amazon Personalize, visit our documentation.