Building Machine Learning Systems with a Feature Store Batch, Real-Time, and LLM Systems (Jim Dowling) (z-library.sk, 1lib.sk, z-lib.sk) (1)

Author： Jim Dowling

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 This book introduces fundamental principles and practices for developing, testing, and operating ML and AI systems at scale. It illustrates how an AI system can be decomposed into independent feature, training, and inference pipelines connected by a shared data layer. Through example ML systems, readers will 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. The book is arranged into 6 logical parts, with each consisting of a group of chapters. Part I (chap. 1~3) introduces the feature-training-inference (FTI) architecture and concludes with a case study. Part II (chap. 4, 5) introduces feature stores for ML and a real-time credit card fraud example that will be covered throughout the book. Part III (chap. 6~9) is about data transformations for AI systems using frameworks such as Pandas, Polars, Apache Spark, Apache Flink, a