AWS Clean Rooms ML (Preview) helps you and your partners apply privacy-enhancing ML to generate predictive insights without having to share raw data with each other. The capability’s first model is specialized to help companies create lookalike segments. With AWS Clean Rooms ML lookalike modeling, you can train your own custom model using your data, and invite your partners to bring a small sample of their records to a collaboration to generate an expanded set of similar records while protecting you and your partner’s underlying data. Healthcare modeling will be available in the coming months.
With AWS Clean Rooms ML, you retain full control and ownership of your trained models, including when to use them to generate lookalike segments with your partners, or when to delete them. Your data is only used to train your model, and is never used for AWS model training. You can use intuitive controls that help you and your partners tune the model’s predictive results. For example, an airline can leverage data about its customers, collaborate with an online booking service, and identify prospect travelers with similar characteristics, without either company sharing their underlying data with the other. AWS Clean Rooms ML removes the need to share data to build, train, and deploy ML models with your partners.
AWS Clean Rooms ML was built and tested across a wide variety of datasets such as e-commerce and streaming video, and can help customers improve accuracy on lookalike modeling by up to 36%, when compared with representative industry baselines. In real-world applications such as prospecting for new customers, this accuracy improvement can translate into savings of million dollars.
AWS Clean Rooms ML (Preview) is available as a capability of AWS Clean Rooms in these AWS Regions. To learn more, visit AWS Clean Rooms ML.