1 link tagged with all of: scalability + machine-learning + feature-store + scylladb
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ShareChat engineers faced scalability issues with their ML feature store, initially unable to handle the required load. After a series of architectural optimizations and a shift in focus, they successfully rebuilt the system to support 1 billion features per second without increasing database capacity.