Alex Merced • 5/24/2026

Modern Feature Stores Beyond Batch Pipelines

This article discusses the evolution of feature stores from batch-only systems to modern architectures supporting streaming feature views for real-time ML. It covers the two-store model (offline for training, online for low-latency inference), the training-serving skew problem, and tools like Feast, Apache Iceberg, and enterprise solutions from Databricks and Vertex AI. Topics include point-in-time correct joins, feature governance, on-demand transformations, and when to introduce a feature store. Aimed at ML engineers and data practitioners building production-ready ML platforms.

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#Feature Store #Batch Pipelines #Streaming Features