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A Blueprint for MLOps/LLMOps for Modern ML Practitioners
O’Reilly Media has released ‘Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems’ by Jim Dowling, widely regarded as the most comprehensive, practical blueprint for building and scaling production ML systems today.
Every ML practitioner knows the pain: a model that works beautifully in development but falls apart in production. This book addresses that core challenge head-on, guiding readers through how to build real, reliable, and scalable AI systems rooted in MLOps and LLMOps best practices.
As a key person in the development of feature stores, Jim helped define the product category through the development of the first API-based feature store as well as influential content and organization of the feature store community. This book captures that expertise in a unified, end-to-end framework, covering everything from batch and real-time systems to agentic/LLM-driven architectures that actually make it to production.
Readers will tackle the hardest part of machine learning: building AI systems around data. They will learn how to transform raw data into features & embeddings and design scalable & reliable AI systems. It also serves as a guide for practitioners through:
- Building batch ML systems for any scale and complexity
- Designing real-time ML systems with shift-left and shift-right feature computation patterns
- Creating agentic ML systems powered by LLMs, tools, and retrieval-augmented generation
- Applying core MLOps principles to develop, deploy, and operate production ML systems end-to-end
Whether you’re a data scientist whose models stall before production or an ML engineer under pressure to scale, or a platform team building AI infrastructure for the future; this book is your playbook.
Availability
Building Machine Learning Systems with a Feature Store is now available through O’Reilly, Amazon, and major book retailers. You can download the digital version and start building production-ready ML systems: Building Machine Learning Systems with a Feature Store
About the Author
Jim Dowling is CEO of Hopsworks and a former Associate Professor at KTH Royal Institute of Technology. He led the development of Hopsworks that includes the first open-source feature store for machine learning. He has a unique background in the intersection of data and AI. For data, he worked at MySQL and later led the development of HopsFS, a distributed file system that won the IEEE Scale Prize in 2017. For AI, his PhD introduced Collaborative Reinforcement Learning, and he developed and taught the first course on Deep Learning in Sweden in 2016. He also released a popular online course on serverless machine learning using Python at serverless-ml.org. This combined background of Data and AI helped him realize the vision of a feature store for machine learning based on general purpose programming languages, rather than the earlier feature store work at Uber on DSLs. He is the organizer of the annual feature store summit conference and the featurestore.org community, as well as co-organizer of PyData Stockholm.
About Hopsworks
Hopsworks is a leading real-time AI Lakehouse, providing an end-to-end solution for developing, deploying, and monitoring AI/ML models at scale, delivering world-leading performance. Known for its innovative feature store and comprehensive toolset, Hopsworks empowers organizations to unlock the full potential of their data and tailormade solutions that accelerates their AI journey, ensuring smooth model development, production, and deployment.
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