Practical MLOps

Preface

Why We Wrote This Book

We’ve both spent most of our careers automating things. When we first met and Alfredo didn’t know Python, Noah suggested automating one task per week. Automation is a core pillar for MLOps, DevOps, and this book throughout. You should take all the examples and opinions in this book in the context of future automation.

If Noah could summarize how he spent 2000–2020, it was automating just about anything he could, from film pipelines to software installation to machine learning pipelines. As an engineering manager and CTO at startups in the Bay Area, he built many data science teams from scratch. As a result, he saw many of the core problems in getting machine learning to production in the early stages of the AI/ML revolution.

Noah has been an adjunct professor at Duke, Northwestern, and UC Davis in the last several years, teaching topics that primarily focus on cloud computing, data science, and machine learning engineering. This teaching and work experience gives him a unique perspective about the issues involved in the real-world deployment of machine learning solutions.

Alfredo has a heavy ops background from his Systems Administrator days, with a similar passion for automation. It is not possible to build resilient infrastructure without push-button automation. There is nothing more gratifying when disaster situations happen than rerunning a script or a pipeline to re-create what crashed.

When COVID-19 hit, it accelerated a question we both had, ...