English | 2023 | ISBN: ‎ 1617298158 | 247 pages | True/Retail EPUB, MOBI | 40.93 MB

Optimize the performance of your systems with practical expents used by eeers in the world’s most competitive industries.

In Expentation for Eeers: 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 expentation rate with multi-armed bandits

Tune multiple parameters expentally with Bayesian optimization

Clearly define business metrics used for decision-making

Identify and avoid the common pitfalls of expentation

Expentation for Eeers: 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 undee revenue or other business metrics. By the you’re done, you’ll be able to seamlessly deploy expents in production while 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? Expentation is the only way to answer questions like these. This unique book reveals sophisticated expentation 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

Expentation for Eeers: 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 expentation strats 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 expentation 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 expentation rate with multi-armed bandits

Tune multiple parameters expentally with Bayesian optimization

About the reader

For ML and software eeers 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 eeer at Instagram. He teaches in the AI and Data Science master's programs at Yeshiva University.

Table of Contents

1 Optimizing systems by expent

2 A/B testing: Evaluating a modification to your system

3 Multi-armed bandits: Maximizing business metrics while expenting

4 Response surface methodology: Optimizing continuous parameters

5 Contextual bandits: Making targeted decisions

6 Bayesian optimization: Automating expental optimization

7 Managing business metrics

8 Practical considerations