Experimentation for Engineers

From A/B Testing to Bayesian Optimization

By: David Sweet

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Paperback | 20 February 2023

At a Glance

Paperback
248 Pages

Dimensions(cm)
18.7 x 23.6 x 1.8

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$123.75

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

Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You will 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.

You will learn how to:

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
Clearly define business metrics used for decision-making
Identify and avoid the common pitfalls of experimentation

By the time you''re done, you will be able to seamlessly deploy experiments in production, whilst avoiding common pitfalls.

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 and will help you enhance machine learning systems, software applications, and quantitative trading solutions.

Industry Reviews

"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

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