Building machine learning systems with a feature store

Summary: Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems. Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML--the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems. With this book, you'll be able to: Make the leap from training ML models to building ML systems Develop an ML system as modular feature, training, and inference pipelines Design, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generation Learn the problems a feature store for ML solves when building ML systems Understand the principles of MLOps for developing and safely updating ML systems Jim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden.