Real-time machine learning is a challenging system's domain, but one where huge value can be created, as shown by companies such as TikTok. The best personalized search and recommendation systems are based on real-time ML with a Feature Store, a Vector Database, and a Model Serving platform, serving recommendations on-demand taking into account user history and context (such as ‘trending’ content).

In this webinar, we will analyze the synergies of an integrated Feature Store and Model Serving platform for the operationalization of real-time ML-enabled services, including the key MLOps principles needed to ensure integrated version management for upgrading and downgrading models and the features that feed them. We will show an implementation of a real-time, personalized recommendation system using Hopsworks.

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