{"product_id":"9781617298158","title":"Experimentation for Engineers: From A\/B testing to Bayesian optimization","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eSweet, David\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e3\/7\/2023\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eOptimize the performance of your systems with practical experiments used by engineers in the world’s most competitive industries.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eExperimentation for Engineers: From A\/B testing to Bayesian optimization\u003c\/i\u003e you will learn how to:\u003cbr\u003e\u003cbr\u003eDesign, run, and analyze an A\/B test\u003cbr\u003eBreak the \"feedback loops\" caused by periodic retraining of ML models\u003cbr\u003eIncrease experimentation rate with multi-armed bandits\u003cbr\u003eTune multiple parameters experimentally with Bayesian optimization\u003cbr\u003eClearly define business metrics used for decision-making\u003cbr\u003eIdentify and avoid the common pitfalls of experimentation\u003cbr\u003e\u003cbr\u003e\u003ci\u003eExperimentation for Engineers: From A\/B testing to Bayesian\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003eDoes 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eExperimentation for Engineers: From A\/B testing to Bayesian optimization\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eDesign, run, and analyze an A\/B test\u003cbr\u003eBreak the “feedback loops” caused by periodic retraining of ML models\u003cbr\u003eIncrease experimentation rate with multi-armed bandits\u003cbr\u003eTune multiple parameters experimentally with Bayesian optimization\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003eFor ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDavid Sweet\u003c\/b\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Optimizing systems by experiment\u003cbr\u003e2 A\/B testing: Evaluating a modification to your system\u003cbr\u003e3 Multi-armed bandits: Maximizing business metrics while experimenting\u003cbr\u003e4 Response surface methodology: Optimizing continuous parameters\u003cbr\u003e5 Contextual bandits: Making targeted decisions\u003cbr\u003e6 Bayesian optimization: Automating experimental optimization\u003cbr\u003e7 Managing business metrics\u003cbr\u003e8 Practical considerations","brand":"Manning","offers":[{"title":"Default Title","offer_id":48176682205439,"sku":"9781617298158","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_8747c1e4-f690-4992-ba59-bb1faf2bae70.jpg?v=1771351332","url":"https:\/\/massivebookshop.com\/products\/9781617298158","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}