Tuning Up
From A/B testing to Bayesian optimization
David Sweet
  • MEAP began December 2020
  • Publication in Summer 2021 (estimated)
  • ISBN 9781617298158
  • 275 pages (estimated)
  • printed in black & white

I would tell my students and colleagues that if they are looking for a great conversation with a knowledgeable friend then they need to pick up a copy of this book!

Roger Le
Master industry-proven experimental methods to deliver continuous improvement to engineered systems.

In Tuning Up: From A/B testing to Bayesian optimization you will learn how to:

  • Design, run, and analyze an A/B test
  • Assess the effectiveness of a new feature
  • Increase experimentation rate with multi-armed bandits
  • Tune multiple parameters experimentally with Bayesian optimization
  • Clearly define business metrics used for decision making
  • Identify and avoid the common pitfalls of experimentation

Tuning Up: From A/B testing to Bayesian optimization is a toolbox of experimental methods that will keep your software and systems working at peak performance. You’ll learn to implement tests and techniques that will boost the effectiveness of machine learning systems, trading strategies, infrastructure, and more. Each method in this practical guide is regularly utilized in highly competitive industries like finance and social media.

About the Technology

Tuning your software and systems is best done by following established methods employed by high-performing teams like the ones led by author David Sweet. This book reveals tests, metrics, and practical tools that will ensure your projects are constantly improving, delivering revenue, and ensuring user satisfaction.

About the book

Tuning Up: From A/B testing to Bayesian optimization teaches you proven methods for improving your production systems. Each method has been tested in industry and is fully explained using basic math and code written in Python and NumPy. The book is filled with real-world use cases for quantitative trading, recommender systems, and social media. You’ll learn how to evaluate changes to your system and explore ways to ensure that your testing is not undermining revenue and other business metrics. By the time you’re done, you’ll be able to seamlessly run effective performance experiments whilst avoiding common mistakes and pitfalls.

What's inside

For Python programmers with knowledge of NumPy and basic statistics.

About the author

David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram, where he used experimental methods to tune trading systems and recommender systems. This book is an extension of his lectures on tuning quantitative trading systems given at NYU Stern over the past three years.

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