{"product_id":"machine-learning-system-design-with-end-to-end-examples-isbn-9781633438750","title":"Machine Learning System Design: With end-to-end examples","description":"\u003cb\u003eGet the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFrom information gathering to release and maintenance, \u003ci\u003eMachine Learning System Design\u003c\/i\u003e guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity.\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eMachine Learning System Design: With end-to-end examples\u003c\/i\u003e you will learn:\u003cul\u003e\n\u003cli\u003eThe big picture of \u003ci\u003emachine learning system design\u003c\/i\u003e\n\u003c\/li\u003e\n\u003cli\u003eAnalyzing a problem space to identify the optimal ML solution\u003c\/li\u003e\n\u003cli\u003eAce ML system design interviews\u003c\/li\u003e\n\u003cli\u003eSelecting appropriate metrics and evaluation criteria\u003c\/li\u003e\n\u003cli\u003ePrioritizing tasks at different stages of ML system design\u003c\/li\u003e\n\u003cli\u003eSolving dataset-related problems with data gathering, error analysis, and feature engineering\u003c\/li\u003e\n\u003cli\u003eRecognizing common pitfalls in ML system development\u003c\/li\u003e\n\u003cli\u003eDesigning ML systems to be lean, maintainable, and extensible over time\u003c\/li\u003e\n\u003c\/ul\u003eAuthors \u003cb\u003eValeri Babushkin\u003c\/b\u003e and \u003cb\u003eArseny Kravchenko\u003c\/b\u003e have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eDesigning and delivering a machine learning system is an intricate multistep process that requires many skills and roles. Whether you’re an engineer adding machine learning to an existing application or designing a ML system from the ground up, you need to navigate massive datasets and streams, lock down testing and deployment requirements, and master the unique complexities of putting ML models into production. That’s where this book comes in.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eMachine Learning System Design\u003c\/i\u003e shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eMetrics and evaluation criteria\u003c\/li\u003e\n\u003cli\u003eSolve common dataset problems\u003c\/li\u003e\n\u003cli\u003eCommon pitfalls in ML system development\u003c\/li\u003e\n\u003cli\u003eML system design interview tips\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFor readers who know the basics of software engineering and machine learning. Examples in Python.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eValerii Babushkin\u003c\/b\u003e is an accomplished data science leader with extensive experience. He currently serves as a Senior Principal at BP. \u003cb\u003eArseny Kravchenko\u003c\/b\u003e is a seasoned ML engineer currently working as a Senior Staff Machine Learning Engineer at Instrumental.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePart 1\u003cbr\u003e1 Essentials of \u003ci\u003emachine learning system design\u003c\/i\u003e\u003cbr\u003e2 Is there a problem?\u003cbr\u003e3 Preliminary research\u003cbr\u003e4 Design document\u003cbr\u003ePart 2\u003cbr\u003e5 Loss functions and metrics\u003cbr\u003e6 Gathering datasets\u003cbr\u003e7 Validation schemas\u003cbr\u003e8 Baseline solution\u003cbr\u003ePart 3\u003cbr\u003e9 Error analysis\u003cbr\u003e10 Training pipelines\u003cbr\u003e11 Features and feature engineering\u003cbr\u003e12 Measuring and reporting results\u003cbr\u003ePart 4\u003cbr\u003e13 Integration\u003cbr\u003e14 Monitoring and reliability\u003cbr\u003e15 Serving and inference optimization\u003cbr\u003e16 Ownership and maintenance","brand":"Simon \u0026 Schuster","offers":[{"title":"Default Title","offer_id":48683372871909,"sku":"NP9781633438750","price":69.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/O_cd16b760-4693-479e-802e-7b45816c01e1.jpg?v=1775169611","url":"https:\/\/k12savings.com\/es\/products\/machine-learning-system-design-with-end-to-end-examples-isbn-9781633438750","provider":"K12savings","version":"1.0","type":"link"}