Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines

Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field.

Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle.

This book provides four in-depth sections that cover all aspects of machine learning engineering:

• Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage
• Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search
• Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging
• Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines

Robert Crowe is a data scientist and TensorFlow enthusiast. Robert has a passion for helping developers quickly learn what they need to be productive. Robert is the Senior Product Manager for TensorFlow Open-Source and MLOps at Google and helps ML teams meet the challenges of creating products and services with ML. Previously, Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs.

Hannes Hapke is a Senior Machine Learning Engineer at Digits, and has co-authored multiple machine learning publications, including the book "Building Machine Learning Pipelines" by O'Reilly Media. He has also presented state-of-the-art ML work at conferences like ODSC or O’Reilly’s TensorFlow World and is an active contributor to TensorFlow's TFX Addons project. Hannes is passionate about machine learning engineering and production machine learning use cases using the latest machine learning developments.

Emily Caveness is a software engineer at Google. She currently works on ML data analysis and validation.

Di Zhu is an engineer at Google. She has worked on a variety of projects, including MLOps infrastructure, applied machine learning solutions for different verticals including vision, ranking, dynamic pricing, etc. She is passionate about using engineering to solve real-world problems, designing and delivering MLOps solutions for several critical Google products and external partners. In addition to professional pursuits, Di is also a tennis player, Latin dancing competitor, and piano player.