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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they’re data dependent, with data varying wildly from one use case to the next. In this book, you’ll 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
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
- Author: O’Reilly Media
- ISBN: 9781098107963
- Format: Paperback
- Publication Date: 2022-06-21
- Page Count: 386
Original price was: $50.00.$40.00Current price is: $40.00.
Description
Transform Your Machine Learning Knowledge Into Real-World Applications
Whether you’re a seasoned data scientist, an engineer dabbling in machine learning, or a professional eager to step into AI production, Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications in paperback offers you the in-depth blueprint you’ve been searching for. Written to bridge the gap between theory and application, this book equips readers with pragmatic techniques to design, implement, and refine ML solutions that are scalable, robust, and efficient.
A Stepwise Guide That Works
Unlike other technical ML resources, this book approaches production readiness through a systematic, iterative process. It breaks down the complexities of machine learning system design, focusing on aspects like model deployment, scalability, and monitoring — areas that often pose challenges to professionals. Its structured methodology speaks to enthusiasts and experts alike, offering solutions that drive business and practical outcomes.
Why This Paperback Must Be Part of Your Collection
If you’ve struggled with implementing machine learning systems in real-world scenarios, this publication is your answer. It covers pivotal topics such as:
- Translating machine learning models into production-ready formats.
- Iterative workflows optimizing both design and usability.
- Best practices to ensure system scalability and performance stability.
- Overcoming common pitfalls encountered during model deployment.
Readable, concise, and packed with industry insights, this paperback introduces key tools and strategies for both individuals and teams embarking on machine learning projects. Add it to your library today and innovate confidently in the AI space.
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