Engineering Online Experimentation and ML Evaluations by Ming... - 9798868827204
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Engineering Online Experimentation and ML EvaluationsArchitecture, Statistics and Machine Learning for Production-Scale Systems\nAuthor(s): Ming Lei\nFormat: Paperback\nPublisher: APress, United States\nImprint: APress\nISBN-13: 9798868827204, 979-8868827204\nSynopsis\nOnline 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.\n\nThe 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 engineeringtraffic 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.
Engineering Online Experimentation and ML Evaluations by Ming... - 9798868827204
Engineering Online Experimentation and ML EvaluationsArchitecture, Statistics and Machine Learning for Production-Scale Systems\nAuthor(s): Ming Lei\nFormat: Paperback\nPublisher: APress, United States\nImprint: APress\nISBN-13: 9798868827204, 979-8868827204\nSynopsis\nOnline 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.\n\nThe 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 engineeringtraffic 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.
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