{"product_id":"machine-learning-engineering-in-action-paperback","title":"Machine Learning Engineering in Action (Paperback)","description":"\u003cp\u003e\u003cspan class=\"a-text-bold\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-weight:\u003eField-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production.\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eIn \u003c\/span\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-style: italic\u003eMachine Learning Engineering in Action\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e, you will learn:\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eEvaluating data science problems to find the most effective solution\u003cbr style=\"box-sizing: border-box;\"\u003eScoping a machine learning project for usage expectations and budget\u003cbr style=\"box-sizing: border-box;\"\u003eProcess techniques that minimize wasted effort and speed up production\u003cbr style=\"box-sizing: border-box;\"\u003eAssessing a project using standardized prototyping work and statistical validation\u003cbr style=\"box-sizing: border-box;\"\u003eChoosing the right technologies and tools for your project\u003cbr style=\"box-sizing: border-box;\"\u003eMaking your codebase more understandable, maintainable, and testable\u003cbr style=\"box-sizing: border-box;\"\u003eAutomating your troubleshooting and logging practices\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eFerrying a machine learning project from your data science team to your end users is no easy task. \u003c\/span\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-style: italic\u003eMachine Learning Engineering in Action\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eBen introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the technology\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eDeliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the book\u003c\/strong\u003e\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-style: italic\u003eMachine Learning Engineering in Action\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eWhat's inside\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eScoping a machine learning project for usage expectations and budget\u003cbr style=\"box-sizing: border-box;\"\u003eChoosing the right technologies for your design\u003cbr style=\"box-sizing: border-box;\"\u003eMaking your codebase more understandable, maintainable, and testable\u003cbr style=\"box-sizing: border-box;\"\u003eAutomating your troubleshooting and logging practices\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eAbout the reader\u003cbr style=\"box-sizing: border-box;\"\u003eFor data scientists who know machine learning and the basics of object-oriented programming.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the Author\u003c\/strong\u003e\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-bold\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-weight:\u003eBen Wilson\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif\u003e is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eTable of Contents\u003c\/span\u003e\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003ePART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e1 What is a machine learning engineer?\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e2 Your data science could use some engineering\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e3 Before you model: Planning and scoping a project\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e4 Before you model: Communication and logistics of projects\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e5 Experimentation in action: Planning and researching an ML project\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e6 Experimentation in action: Testing and evaluating a project\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e7 Experimentation in action: Moving from prototype to MVP\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003ePART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e9 Modularity for ML: Writing testable and legible code\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e10 Standards of coding and creating maintainable ML code\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e11 Model measurement and why it’s so important\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e12 Holding on to your gains by watching for drift\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e13 ML development hubris\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003ePART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e14 Writing production code\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e15 Quality and acceptance testing\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e16 Production infrastructure\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEbook License\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eEnd-User Warranty And License Agreement\u003c\/p\u003e\u003cp\u003e1. 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