Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems

by Jim Dowling

Overview

Get up to speed on a new unified approach to building machine learning (ML) systems with a feature store. Using this practical book, data scientists and ML engineers will learn in detail how to develop and operate batch, real-time, and agentic ML systems.

Author Jim Dowling introduces fundamental principles and practices for developing, testing, and operating ML and AI systems at scale. You'll see how any AI system can be decomposed into independent feature, training, and inference pipelines connected by a shared data layer. Through example ML systems, you'll tackle the hardest part of ML systems--the data, learning how to transform data into features and embeddings, and how to design a data model for AI.

• Develop batch ML systems at any scale

• Develop real-time ML systems by shifting left or shifting right feature computation

• Develop agentic ML systems that use LLMs, tools, and retrieval-augmented generation

• Understand and apply MLOps principles when developing and operating ML systems

Product Details

ISBN-13:	9781098165192
Publisher:	O'Reilly Media, Incorporated
Publication date:	11/06/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	508
File size:	14 MB
Note:	This product may take a few minutes to download.

About the Author

Jim Dowling is CEO of Hopsworks and an Associate Professor at KTH Royal Institute of Technology. He's 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 was the first evangelist for feature stores, helping to create the feature store product category through talks at industry conferences, like Data/AI Summit, PyData, OSDC, and educational articles on feature stores. He is the organizer of the annual feature store summit conference and the featurestore.org community, as well as co-organizer of PyData Stockholm.