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.

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.