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Mark Treveil - Introducing MLOps

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Mark Treveil Introducing MLOps

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Introducing MLOps by Mark Treveil and the Dataiku Team Copyright 2020 - photo 1
Introducing MLOps

by Mark Treveil , and the Dataiku Team

Copyright 2020 Dataiku. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

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  • December 2020: First Edition
Revision History for the First Edition
  • 2020-11-30: First Release

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The OReilly logo is a registered trademark of OReilly Media, Inc. Introducing MLOps, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

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This work is part of a collaboration between OReilly and Dataiku. See our statement of editorial independence .

978-1-492-08329-0

[LSI]

Preface

Weve reached a turning point in the story of machine learning where the technology has moved from the realm of theory and academics and into the real worldthat is, businesses providing all kinds of services and products to people across the globe. While this shift is exciting, its also challenging, as it combines the complexities of machine learning models with the complexities of the modern organization.

One difficulty, as organizations move from experimenting with machine learning to scaling it in production environments, is maintenance. How can companies go from managing just one model to managing tens, hundreds, or even thousands? This is not only where MLOps comes into play, but its also where the aforementioned complexities, both on the technical and business sides, appear. This book will introduce readers to the challenges at hand, while also offering practical insights and solutions for developing MLOps capabilities.

Who This Book Is For

We wrote this book specifically for analytics and IT operations team managers, that is, the people directly facing the task of scaling machine learning (ML) in production. Given that MLOps is a new field, we developed this book as a guide for creating a successful MLOps environment, from the organizational to the technical challenges involved.

How This Book Is Organized

This book is divided into three parts. The first is an introduction to the topic of MLOps, diving into how (and why) it has developed as a discipline, who needs to be involved to execute MLOps successfully, and what components are required.

The second part roughly follows the machine learning model life cycle, with chapters on developing models, preparing for production, deploying to production, monitoring, and governance. These chapters cover not only general considerations, but MLOps considerations at each stage of the life cycle, providing more detail on the topics touched on in .

The final part provides tangible examples of how MLOps looks in companies today, so that readers can understand the setup and implications in practice. Though the company names are fictitious, the stories are based on real-life companies experience with MLOps and model management at scale.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, email addresses, filenames, and file extensions.

Constant width

Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.

Constant width bold

Shows commands or other text that should be typed literally by the user.

Constant width italic

Shows text that should be replaced with user-supplied values or by values determined by context.

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Acknowledgments

We would like to thank the entire Dataiku team for their support in developing this book, from conception to completion. Its been a true team effort and, like most things we do at Dataiku, rooted in fundamental collaboration between countless people and teams.

Thanks to those who supported our vision from the beginning of writing this book with OReilly. Thanks to those who stepped in to help with writing and editing. Thanks to those who provided honest feedback (even when it meant more writing and rewriting and re-rewriting). Thanks to those who were internal cheerleaders and, of course, those who helped us promote the finished product to the world.

Part I. MLOps: What and Why
Chapter 1. Why Now and Challenges

Machine learning operations (MLOps) is quickly becoming a critical component of successful data science project deployment in the enterprise (). Its a process that helps organizations and business leaders generate long-term value and reduce risk associated with data science, machine learning, and AI initiatives. Yet its a relatively new concept; so why has it seemingly skyrocketed into the data science lexicon overnight? This introductory chapter delves into what MLOps is at a high level, its challenges, why it has become essential to a successful data science strategy in the enterprise, and, critically, why it is coming to the forefront now.

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