Many companies must tackle the difficult use case of building a highly optimized recommender system. The challenge comes from processing large volumes of data to train and tune the model daily with new data and then make predictions based on user behavior during an active engagement. In this post, we show you how to use Amazon SageMaker Feature Store, a purpose-built repository where you can store, access, and share model features across teams in your company. With both online and offline Feature Store, you can address the complex task of creating a product recommendation engine based on consumer behavior. This post comes with an accompanying workshop and GitHub repo.
The post and workshop are catered towards data scientists and expert machine learning (ML) practitioners who need to build custom models. For the non-data scientist or ML expert audience, check out our AI service Amazon Personalize, which allows developers to build

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