Online experimentation is now essential for modern software and machine learning teams. This book provides an engineer-first, end-to-end guide to building and operating production-ready experimentation platforms.

The book begins with Part I establishing the core foundations of credible experimentation, including hypothesis testing, power analysis, sample sizing, metric design, and common pitfalls such as peeking, multiple testing, and novelty or learning effects. Part II focuses on platform engineering—traffic and identity management, mutual exclusion, event and logging design, ETL/ELT pipelines, building a stats engine with SciPy and statsmodels, SRM detection, integrating deployments with feature flags and canaries, and setting up guardrail and health monitoring. Part III presents advanced designs that improve speed and sensitivity: sequential testing with alpha spending, bootstrap intervals for ratios and quantiles, A/B/n testing with ANOVA, interleaving for ranking systems, switchback and geo experiments, and multi-armed bandits. Part IV connects experimentation to ML workflows, covering offline, shadow, canary, and A/B evaluation pipelines; Bayesian optimization for adaptive experimentation; counterfactual and IPS methods for learning from logs; and safe retraining supported by strong governance.

What you will learn:

• Design trustworthy experiments with proper metrics, guardrails, α/power/MDE settings, and safeguards against peeking and multiple-testing errors
• Build a production-ready experimentation stack with assignment, identity/diversion, logging, ETL/ELT, a stats engine, and SRM checks
• Run advanced designs at scale, including sequential tests, bootstrap CIs, interleaving, switchback/geo experiments, and multi-armed bandits
• Evaluate ML systems from offline to online, leverage experiment logs for learning, and enable safe retraining with governance

Who this book is for:

The primary audience for this book includes Data Engineers, ML Engineers, and Platform or Software Architects. It is also well suited for Product and Data Scientists who want a deeper understanding of experimentation systems and the engineering principles behind them.

Ming Lei is a data and ML engineering leader with 20 years of experience building end-to-end ML systems for Internet Ads and Search, and experimentation platforms for E-commerce. He has designed large-scale systems that operationalize rigorous statistical methods — such as sequential testing, bootstrapping, multi-armed bandits, and Bayesian optimization — and support ML evaluation from offline analysis to online deployment. His leadership spans roles at eBay, Meta (Facebook), Google, and Appen. He holds multiple US patents and advanced degrees in computer science (UC Riverside) and economics (Clark University), along with a B.S. in physics (Wuhan University). He is based in the Northwest of US.