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.