In this talk, we will delve into the significance of feature stores in the era of machine learning that we are entering. Complex features such as embeddings begin to replace certain traditional features. Vector databases are added to the mix. Drawing on my experience working on recommender systems that employed deep learning models that used both traditional features and embeddings, I will showcase how these two worlds combine and where feature stores fit in. By the end of the talk, attendees will gain insights into the importance of feature stores in modern machine learning workflows and how they can be effectively used in a Post-GPT world.
Founder & CEO
02:10PM - Day 1