Название: Tuning Up: From A/B testing to Bayesian optimization (MEAP)
Автор: Dawid Sweet
Издательство: Manning Publications
Год: 2020
Формат: PDF, MOBI
Страниц: 107
Размер: 10 Mb
Язык: English

Master industry-proven tests, methods, and evaluative experiments to deliver continuous improvements to your software.
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 experimentationv

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 software and data systems. Each method has been tested in industry, and is fully explained with easy-to-understand math and Python code—no black boxes you just have to trust are working! 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.