Experimentation for Engineers you own this product

From A/B testing to Bayesian optimization
David Sweet
  • MEAP began December 2020
  • Publication in February 2023 (estimated)
  • ISBN 9781617298158
  • 248 pages (estimated)
  • printed in black & white
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Remember this book when you are trying to get A/B tests up and running!

Richard Vaughan
Look inside
Learn practical and modern experimental methods used by engineers in technology and trading.

In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to:

  • Design, run, and analyze an A/B test
  • Break the "feedback loops" cause by periodic retraining of ML models
  • 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

Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox for optimizing machine learning systems, quantitative trading strategies, and more. You’ll start with a deep dive into tests like A/B testing, and then graduate to advanced techniques used to measure performance in highly competitive industries like finance and social media. The tests in this unique, practical guide will quickly reveal which approaches and features deliver real results for your business.

about the technology

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

about the book

Experimentation for Engineers: From A/B testing to Bayesian optimization teaches you reliable techniques for evaluating new features and fine-tuning parameters. You’ll learn to optimize production systems with methods that have been proven in highly competitive environments. Each method is fully explained using basic math and Python code, and illustrated with real-world use cases in quantitative trading, recommender systems, and ad serving.

You’ll learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls.

about the reader

For ML engineers, quantitative traders, and software engineers looking to extract the most value from their systems. Examples in Python and NumPy.

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 at NYU Stern and is the basis for his course, Experimental Optimization, at Yeshiva University.

FREE domestic shipping on orders of three or more print books

Experimentation for Engineers is a well organized and written book that educates the reader on effective techniques for evaluating features and tuning parameters; both essential aspects of ML.

Dan Sheikh

There are some books we wish were available a long time ago. This is one of them, bridging gaps between theory and practice, polishing skills for more real-world endeavours.

Eric Platon

The books offers great exposition to statistical experimentation of ML systems, needed in a plethora of industries.

Ioannis Atson
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