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epub | 36.43 MB | English | Isbn:9781617298158 | Author: David Sweet | Year: 2023
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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.
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
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.
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
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