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Item #:
57403947
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
90% of respondents would recommend this to a friend
Item #:
57403947
TND 193
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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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Customer Questions & Answers
-
Question:
What is the primary focus of this book?
Answer: The book focuses on designing reliable, scalable, and maintainable machine learning systems adapted to changing environments. -
Question:
Are there real-life examples included in the book?
Answer: Yes, the book includes actual case studies to illustrate the iterative design framework. -
Question:
Who is the author of this book?
Answer: The author is Chip Huyen, co-founder of Claypot AI.
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Pros
- Offers practical design insights
- Covers important ML concepts
- Good for beginners
- Includes real-world examples
Cons
- Lacks depth in advanced topics
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TND 193
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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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