Preface
If you are in the process of deploying large-scale data systems into production or if you are using large-scale data in production now, this book is for you. In it we address the difference in big data hype versus serious large-scale projects that bring real value in a wide variety of enterprises. Whether this is your first large-scale data project or you are a seasoned user, you will find helpful content that should reinforce your chances for success.
Here, we speak to business team leaders; CIOs, CDOs, and CTOs; business analysts; machine learning and artificial intelligence (AI) experts; and technical developers to explain in practical terms how to take big data analytics and machine learning/AI into production successfully. We address why this is challenging and offer ways to tackle those challenges. We provide suggestions for best practice, but the book is intended as neither a technical reference nor a comprehensive guide to how to use big data technologies. You can understand it regardless of whether you have a deep technical background. That said, we think that you’ll also benefit if you’re technically adept, not so much from a review of tools as from fundamental ideas about how to make your work easier and more effective.
The book is based on our experience and observations of real-world use cases so that you can gain from what has made others successful.
How to Use This Book
Use the first two chapters to gain an understanding of the goals and challenges and some of the potential pitfalls of deploying to production (Chapter 1) and for guidance on how to best approach the design, planning, and execution of large data systems for production (Chapter 2). You will learn how to reduce risk while maintaining innovative approaches and flexibility. We offer a pragmatic approach, taking into account that winning systems must be cost effective and make sense as sustainable, practical, and profitable business solutions.
From there, the book digs into specific examples, based on real-world experience with customers who are successfully using big data in production. Chapter 3 focuses on the special case of machine learning and AI in production, given that this topic is gaining in widespread popularity. Chapter 4 describes an example technology of a data platform with the necessary technical capabilities to support emerging trends for large-scale data in production.
With this foundational knowledge in hand, you’ll be set in the last part of the book to explore in Chapter 5 a range of design patterns that are working well for the customers in production we see across various sectors. You can customize these patterns to fit your own needs as you build and adapt production systems. Chapter 6 offers a variety of specific tips for best practice and how to avoid “gotchas” as you move to production.
We hope you find this content makes production easier and more effective in your own business setting.
—Ted Dunning and Ellen Friedman
September 2018