This is not an issue. We are currently working on developing a scalable architecture for our ranking system using Feast. As a backend, we are utilizing GCP for the offline store and Redis for the online store in AWS, which is in close proximity to our serving environment.

Feast effectively abstracts the feature vectors for classical models or batch inference. However, the complexity arises when we integrate embeddings into our recommendation system.

While Feast serves well for training the embeddings model and encoding the embeddings in offline batches, the challenge lies in serving these embeddings. My question pertains to how a vector search solution fits into our architecture. Where should the embeddings reside, and do we need to register them initially?

In essence, considering our two-tower ranking model where the first tower's embeddings are encoded offline and the second tower's embeddings are encoded upon request, followed by a search against the pre-encoded embeddings, how can we structure this using Feast?