Explore the implementation of personalized recommendations and search systems at scale using retrieval and ranking architectures based on the two-tower embedding model. Dive into the offline and online infrastructure required for building a high-performance recommendation service on the open-source Hopsworks platform. Learn how to train models, index items in an embedding store, update feature stores, and implement similarity search for candidate retrieval. Discover techniques to maintain end-to-end latencies below 100ms while ensuring high availability of all system components. Gain insights from Jim Dowling, CEO of Logical Clocks and lead architect of the Hopsworks platform, as he guides you through the complexities of building scalable, personalized recommendation systems.