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57403947
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
90% of respondents would recommend this to a friend
Item #:
57403947
€ 53
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Learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments.
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What Stands Out
Iterative Approach
Offers a step-by-step guide that emphasizes the iterative nature of developing machine learning systems, ensuring a practical framework for building production-ready applications.
Comprehensive Coverage
Covers a wide array of topics, from initial design to deployment, making it suitable for both beginners and experienced practitioners looking for in-depth knowledge.
Real-World Examples
Includes case studies and real-world applications that illustrate best practices in machine learning, enhancing the reader's ability to apply concepts to their own projects.
Product Details
- 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
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | June, 2022 |
| Pages | 386 |
| Reading level | Professional and Scholarly |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 1 |
| Publisher | O'Reilly Media |
| Original languages | English |
| Language | English |
| Is collectible | N |
| Editor | Vaishali V Phalke, Mohannad Ibrahim, Douglas J Quint, Hemant Parmar, Gaurang Shah, Sachin Gujar |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.90 x 0.70 x 9.10 in (17.5 x 1.8 x 23.1 cm) |
| Assembled product weight | 1.35 lb (610 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Aspiring Data Scientists
Provides foundational knowledge and practical steps to develop machine learning applications effectively.
-
Tech Professionals
Ideal for software engineers seeking to incorporate machine learning into existing systems in a structured manner.
-
Project Managers
Useful for overseeing machine learning projects with a strong focus on iterative improvement and production readiness.
-
Beginners in ML
May be too advanced for those without prior knowledge of machine learning or programming concepts.
Product Description
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Chip Huyen All Books Editorial Review
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications is a comprehensive paperback guide published by O'Reilly Media in June 2022. With 386 pages dedicated to the field of computing and internet, this non-fiction resource is ideal for professionals and scholars interested in data science. The editors, including experts such as Vaishali V Phalke and Mohannad Ibrahim, ensure that the content is both authoritative and practical. This book focuses on the iterative process essential for designing robust machine learning systems, making it a vital read for anyone looking to enhance their skills in this rapidly evolving field.
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Pros
- Ideal for professionals and scholars in data science
- Comprehensive coverage of machine learning design
- Contributions from multiple expert editors
- Focused on production-ready applications
- Clear and structured iterative process
Cons
- May not suit casual readers or beginners
Product Price History
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Features & Benefits
- Discover a comprehensive framework for designing machine learning systems.
- Focus on reliability, scalability, and adaptability to meet business needs.
- Learn to process and create effective training data.
- Automate model development, evaluation, and deployment processes.
- Implement effective monitoring systems for production environments.
- Address unique challenges in machine learning with real case studies.
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