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
From the brand
Machine Learning, AI & more
Machine Learning
Artificial Intelligence
Deep Learning
Language Processing (NLP, LLM)
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ASIN : B0G19MT9SP
Publisher : O’Reilly Media
Accessibility : Learn more
Publication date : November 6, 2025
Edition : 1st
Language : English
File size : 12.6 MB
Simultaneous device usage : Unlimited
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 820 pages
ISBN-13 : 978-1098165192
Page Flip : Enabled
Best Sellers Rank: #993,854 in Kindle Store (See Top 100 in Kindle Store) #189 in Data Modeling & Design (Kindle Store) #195 in Computer Systems Analysis & Design (Books) #238 in Natural Language Processing (Kindle Store)