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 scaleDevelop real-time ML systems by shifting left or shifting right feature computationDevelop agentic ML systems that use LLMs, tools, and retrieval-augmented generationUnderstand and apply MLOps principles when developing and operating ML systems

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 scaleDevelop real-time ML systems by shifting left or shifting right feature computationDevelop agentic ML systems that use LLMs, tools, and retrieval-augmented generationUnderstand and apply MLOps principles when developing and operating ML systems

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 scaleDevelop real-time ML systems by shifting left or shifting right feature computationDevelop agentic ML systems that use LLMs, tools, and retrieval-augmented generationUnderstand and apply MLOps principles when developing and operating ML systems