The real-time recommendations engine in Tiktok, Monolith, is so good it has been described as "digital crack" (by Andrej Karpathy, former head of AI at Tesla). In this tutorial, we will build the core components of Tiktok Monolith (a retrieval and ranking architecture): a stream processing feature pipeline, a two-tower embedding model to support personalized queries based on each user's history/context, and a simple user interface in Python (Streamlit). Our real-time machine learning system will consist of 3 Python programs - the feature pipeline, the training pipeline, and the online inference pipeline - and the ML infrastructure they require will be provided by the open-source Hopsworks platform, including a feature store, vector database, model serving, and model registry.

Jim Dowling

Affiliation: Hopsworks

Jim Dowling is CEO of Hopsworks and an Associate Professor at KTH Royal Institute of Technology. He is lead architect of the open-source Hopsworks platform, a horizontally scalable data platform for machine learning that includes the industry's first Feature Store. He is writing a book for O'Reilly on ML Systems with a feature store.

visit the speaker at: Github • Homepage