How Databricks Feature Store serves features with sub-second freshness

This post details how the Databricks Feature Store achieves sub-second freshness for serving features, likely covering architectural improvements, data pipeline optimizations, and caching strategies to ensure low-latency feature availability for real-time ML applications.