Building Machine Learning Systems with a Feature Store 1st Edition

Capture the edge in modern machine learning with Building Machine Learning Systems with a Feature Store, 1st Edition by Jim Dowling — a practical, hands-on guide that turns complex MLOps concepts into deployable systems. Whether you’re an ML engineer, data scientist, or platform architect, this book shows how feature stores become the backbone of reliable, scalable ML in production.

Discover clear, actionable coverage of feature store design, feature pipelines, real-time and batch feature serving, data validation, lineage, and governance. Dowling blends engineering best practices with architecture patterns and real-world examples so teams can reduce deployment risk, shorten time-to-value, and maintain model performance across environments. The book emphasizes reproducibility, operational monitoring, and integration with modern data stacks and cloud platforms — making it relevant for organizations and data teams worldwide.

You’ll learn to standardize feature development, avoid common pitfalls like data leakage, and implement efficient feature reuse that accelerates model iteration. Written for practitioners and technical leaders, the tone is pragmatic and example-driven, helping you translate theory into production-ready systems that scale across industries.

Equip your team with the know-how to build robust, governed feature stores that support production-grade ML. If you’re committed to building reliable ML infrastructure and improving model velocity, Building Machine Learning Systems with a Feature Store by Jim Dowling is an essential addition to your library. Order now to start designing feature-driven ML systems that perform in production.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.