Experimentation for Engineers - by David Sweet (Paperback)

About the Book

Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox to help you take a deep dive into methods like A/B testing. With sophisticated experimentation practices developed in the world's most competitive industries, the book will help you enhance machine learning systems, software applications, and quantitative trading solutions.

Book Synopsis

Optimize the performance of your systems with practical experiments used by engineers in the world's most competitive industries.

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" caused 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 of techniques for evaluating new features and fine-tuning parameters. You'll start with a deep dive into methods like A/B testing, and then graduate to advanced techniques used to measure performance in industries such as finance and social media. 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Does my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world's most competitive industries that will help you enhance machine learning systems, software applications, and quantitative trading solutions.

About the book
Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimizing software systems. You'll start by learning the limits of A/B testing, and then graduate to advanced experimentation strategies that take advantage of machine learning and probabilistic methods. The skills you'll master in this practical guide will help you minimize the costs of experimentation and quickly reveal which approaches and features deliver the best business results.

What's inside

Design, run, and analyze an A/B test
Break the "feedback loops" caused by periodic retraining of ML models
Increase experimentation rate with multi-armed bandits
Tune multiple parameters experimentally with Bayesian optimization

About the reader
For ML 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. He teaches in the AI and Data Science master's programs at Yeshiva University.

Table of Contents
1 Optimizing systems by experiment
2 A/B testing: Evaluating a modification to your system
3 Multi-armed bandits: Maximizing business metrics while experimenting
4 Response surface methodology: Optimizing continuous parameters
5 Contextual bandits: Making targeted decisions
6 Bayesian optimization: Automating experimental optimization
7 Managing business metrics
8 Practical considerations

From the Back Cover

Learn how to evaluate the changes you make to your system and ensure that your testing does not undermine revenue or other business metrics.

Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimising software systems. From learning the limits of A/B testing to advanced experimentation strategies involving machine learning and probabilistic methods, this practical guide will help you master the skills. It will also help you minimise the costs of experimentation and will quickly reveal which approaches and features deliver the best business results.

What's inside

• Design, run, and analyse an A/B test
• Break the "feedback loops" caused by periodic retraining of ML models
• Increase experimentation rate with multi-armed bandits
• Tune multiple parameters experimentally with Bayesian optimisation

About the reader

For ML and software engineers looking to extract the most value from their systems. Examples are found in Python and NumPy.

Review Quotes

"Putting an 'improved' version of a system into production can be really risky. This book focuses you on what is important!"
Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland

"A must-have for anyone setting up experiments, from A/B tests to contextual bandits and Bayesian optimization."
Maxim Volgin, KLM

"Shows a non-mathematical programmer exactly what they need to write powerful mathematically-based testing algorithms."
Patrick Goetz, The University of Texas at Austin

"Gives you the tools you need to get the most out of your experiments."
Marc-Anthony Taylor, Raiffeisen Bank International

About the Author

David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram. He teaches in the AI and Data Science Master's programmes at Yeshiva University.