{"product_id":"machine-learning-system-design-with-end-to-end-examples-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 \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eMachine Learning System Design: With end-to-end examples\u003c\/i\u003e you will learn: \u003cp\u003e\u003c\/p\u003e- The big picture of machine learning system design\u003cbr\u003e - Analyzing a problem space to identify the optimal ML solution\u003cbr\u003e - Ace ML system design interviews\u003cbr\u003e - Selecting appropriate metrics and evaluation criteria\u003cbr\u003e - Prioritizing tasks at different stages of ML system design\u003cbr\u003e - Solving dataset-related problems with data gathering, error analysis, and feature engineering\u003cbr\u003e - Recognizing common pitfalls in ML system development\u003cbr\u003e - Designing ML systems to be lean, maintainable, and extensible over time \u003cp\u003e\u003c\/p\u003e Authors \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. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Designing 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e- Metrics and evaluation criteria\u003cbr\u003e - Solve common dataset problems\u003cbr\u003e - Common pitfalls in ML system development\u003cbr\u003e - ML system design interview tips \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For readers who know the basics of software engineering and machine learning. Examples in Python. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Part 1\u003cbr\u003e 1 Essentials of machine learning system design\u003cbr\u003e 2 Is there a problem?\u003cbr\u003e 3 Preliminary research\u003cbr\u003e 4 Design document\u003cbr\u003e Part 2\u003cbr\u003e 5 Loss functions and metrics\u003cbr\u003e 6 Gathering datasets\u003cbr\u003e 7 Validation schemas\u003cbr\u003e 8 Baseline solution\u003cbr\u003e Part 3\u003cbr\u003e 9 Error analysis\u003cbr\u003e 10 Training pipelines\u003cbr\u003e 11 Features and feature engineering\u003cbr\u003e 12 Measuring and reporting results\u003cbr\u003e Part 4\u003cbr\u003e 13 Integration\u003cbr\u003e 14 Monitoring and reliability\u003cbr\u003e 15 Serving and inference optimization\u003cbr\u003e 16 Ownership and maintenance\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eValerii Babushkin\u003c\/b\u003e is an accomplished data science leader with extensive experience in the tech industry. He currently serves as the VP of Data Science at Blockchain.com, where he is responsible for leading the company's data-driven initiatives. Prior to joining Blockchain.com, Valerii held key roles at leading tech companies, such as Facebook, Alibaba, and X5 Retail Group. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eArseny Kravchenko\u003c\/b\u003e is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for startups, including real-time video processing, manufacturing optimization, and financial transactions analysis.\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":51181951680786,"sku":"9781633438750","price":55.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_05e3a47e-f32a-4016-b609-a5edf04dbc99.jpg?v=1744410561","url":"https:\/\/surprise-castle.myshopify.com\/products\/machine-learning-system-design-with-end-to-end-examples-9781633438750","provider":"Surprise Castle","version":"1.0","type":"link"}