Hannes Hapke - Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

I wish this book had existed when I started working in production ML! Its an outstanding resource for getting a comprehensive view of production ML systems in general, and TFX in particular. Hannes and Catherine have worked directly with the TensorFlow team to get the most accurate information available for including in this book, and then explained it in clear, concise explanations and examples.

Robert Crowe, TensorFlow Developer Advocate, Google

The data science practitioner knows that real-world machine learning involves more than just machine learning model training. This book demystifies the hidden technical debt in modern machine learning workflows such that you can put the lab and factory data science patterns into production as repeatable workflows.

Josh Patterson, CEO, Patterson Consulting, Coauthor of Deep Learning: A Practitioners Approach
and Kubeflow Operations Guide

This is definitely the book to read if you would like to understand how to build ML pipelines that are automated, scalable, and reproducible! You will learn something useful from it whether you are a data scientist, machine learning engineer, software engineer, or DevOps. It also covers the latest features of TFX and its components.

Margaret Maynard-Reid, Machine Learning Engineer, Tiny Peppers, ML GDE (Google Developer Expert), GDG Seattle Lead Organizer

Wonderfully readable, Building Machine Learning Pipeline serves not only as a comprehensive guide to help data scientists and ML engineers build automated and reproducible ML pipelines, but it is also the only authoritative book on the subject. The book provides an overview of the clearly defined components needed to architect ML pipelines successfully and walks you through hands-on code examples in a practical manner. "

Adewale Akinfaderin, Data Scientist, Amazon Web Services

I really enjoyed reading Building Machine Learning Pipelines. Having used TFX for several years internally at Google as it was growing, I must say I wish I had your book back then instead of figuring this all out on my own. You would have saved me many months of effort and confusion. Thanks for writing such a high quality guide!

Lucas Ackerknecht, Machine Learning Specialist, Anti-Abuse Machine Learning, Google

We all have some of these amazing prototype models lying around. This book will introduce you to the tools and techniques that will help you take that prototype to production. Not only that but you will also build a complete end-to-end pipeline around it so that any future enhancements get delivered automatically and smoothly. This is a great book for beginners in ML ops who want to take their skills to the next level and collaborate with larger teams to help realize the values of innovative new models.

Vikram Tiwari, Cofounder, Omni Labs, Inc.

As a person who had only used TensorFlow as a framework for training deep learning models, when reading this book I was amazed at the pipeline capabilities that the TensorFlow ecosystem has to offer. This book is a great guide to all of the tools for analyzing and deploying models available with TFX, and is easy to read and use for people looking to make their first machine learning pipeline with TensorFlow.

Dr. Jacqueline Nolis, Principal Data Scientist, Brightloom and Coauthor of Build a Career in Data Science

This book is an exceptional deep-dive into Machine Learning Engineering. You will find cogent and practical examples of what it takes to build production-ready ML infrastructure. I would consider this required reading for any engineer or data scientist who intends to apply ML to real-world problems.

Leigh Johnson, Staff Engineer, Machine Learning Services, Slack