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-Titulo Original : Designing Machine Learning Systems An Iterative Process For Production-ready Applications
-Fabricante :
OReilly Media
-Descripcion Original:
Review This is, simply, the very best book you can read about how to build, deploy, and scale machine learning models at a company for maximum impact. Chip is a masterful teacher, and the breadth and depth of her knowledge is unparalleled. - Josh Wills, Software Engineer at WeaveGrid and former Director of Data Engineering, Slack There is so much information one needs to know to be an effective machine learning engineer. Its hard to cut through the chaff to get the most relevant information, but Chip has done that admirably with this book. If you are serious about ML in production, and care about how to design and implement ML systems end to end, this book is essential. - Laurence Moroney, AI and ML Lead, Google One of the best resources that focuses on the first principles behind designing ML systems for production. A must-read to navigate the ephemeral landscape of tooling and platform options. - Goku Mohandas, Founder of Made With ML Chips manual is the book we deserve and the one we need right now. In a blooming but chaotic ecosystem, this principled view on end-to-end ML is both your map and your compass: a must-read for practitioners inside and outside of Big Tech-especially those working at reasonable scale. This book will also appeal to data leaders looking for best practices on how to deploy, manage, and monitor systems in the wild. - Jacopo Tagliabue, Director of AI, Coveo; Adj. Professor of MLSys, NYU Chip is truly a world-class expert on machine learning systems, as well as a brilliant writer. Both are evident in this book, which is a fantastic resource for anyone looking to learn about this topic. - Andrey Kurenkov, PhD Candidate at the Stanford AI Lab Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because theyre data dependent, with data varying wildly from one use case to the next. In this book, youll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems From the Author Ever since the first machine learning course I taught at Stanford in 2017, many people have asked me for advice on how to deploy ML models at their organizations. These questions can be generic, such as What model should I use? How often should I retrain my model? How can I detect data distribution shifts? How do I ensure that the features used during training are consistent with the features used during inference? These questions can also be specific, such as Im convinced that switching from batch prediction to online prediction will give our model a performance boost, but how do I convince my manager to let me do so? or Im the most senior data scientist at my company and Ive recently been tasked with setting up our first machine learning platform; where do I start? My short answer to all these questions is always: It depends. My long answers often involve hours of discussion to understand where the questioner comes from, what theyre actually trying to achieve, and the pros and cons of di
-Fabricante :
OReilly Media
-Descripcion Original:
Review This is, simply, the very best book you can read about how to build, deploy, and scale machine learning models at a company for maximum impact. Chip is a masterful teacher, and the breadth and depth of her knowledge is unparalleled. - Josh Wills, Software Engineer at WeaveGrid and former Director of Data Engineering, Slack There is so much information one needs to know to be an effective machine learning engineer. Its hard to cut through the chaff to get the most relevant information, but Chip has done that admirably with this book. If you are serious about ML in production, and care about how to design and implement ML systems end to end, this book is essential. - Laurence Moroney, AI and ML Lead, Google One of the best resources that focuses on the first principles behind designing ML systems for production. A must-read to navigate the ephemeral landscape of tooling and platform options. - Goku Mohandas, Founder of Made With ML Chips manual is the book we deserve and the one we need right now. In a blooming but chaotic ecosystem, this principled view on end-to-end ML is both your map and your compass: a must-read for practitioners inside and outside of Big Tech-especially those working at reasonable scale. This book will also appeal to data leaders looking for best practices on how to deploy, manage, and monitor systems in the wild. - Jacopo Tagliabue, Director of AI, Coveo; Adj. Professor of MLSys, NYU Chip is truly a world-class expert on machine learning systems, as well as a brilliant writer. Both are evident in this book, which is a fantastic resource for anyone looking to learn about this topic. - Andrey Kurenkov, PhD Candidate at the Stanford AI Lab Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because theyre data dependent, with data varying wildly from one use case to the next. In this book, youll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems From the Author Ever since the first machine learning course I taught at Stanford in 2017, many people have asked me for advice on how to deploy ML models at their organizations. These questions can be generic, such as What model should I use? How often should I retrain my model? How can I detect data distribution shifts? How do I ensure that the features used during training are consistent with the features used during inference? These questions can also be specific, such as Im convinced that switching from batch prediction to online prediction will give our model a performance boost, but how do I convince my manager to let me do so? or Im the most senior data scientist at my company and Ive recently been tasked with setting up our first machine learning platform; where do I start? My short answer to all these questions is always: It depends. My long answers often involve hours of discussion to understand where the questioner comes from, what theyre actually trying to achieve, and the pros and cons of di
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