Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications Paper Cover – May 3, 2024
UZS 604240
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This is simply the best book you can read about how to develop, deploy, scale machine learning models in a company for the greatest impact.
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What Stands Out
Mahsulot tafsilotlari
| Publishers | Iceberg Publishing House |
| Date of publication | May 3, 2024 |
| Prints | 1. |
| Language | Turkish |
| Print Length | 416 pages |
| ISBN-10 | 6259855230 |
| ISBN-13 | 978-6259855233 |
| Dimensions | 20.8 x 1.8 x 13.8 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to enhance their skills in creating production-ready machine learning systems iteratively.
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Machine Learning Engineers
Beneficial for ML engineers needing a structured approach to deploy machine learning applications efficiently and effectively.
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AI Researchers
Useful for AI researchers focused on practical implementations and iterative processes in real-world machine learning applications.
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Beginners
Not suitable for beginners lacking foundational knowledge in machine learning concepts and systems.
MAHSULOT TAVSIFI
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications Paper Cover – May 3, 2024
Mijozlarning savol-javoblari
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Savol:
What is the main focus of the book?
Javob: The book focuses on designing reliable, scalable, and adaptable machine learning systems. -
Savol:
Who is the author of the book?
Javob: The author is Chip Huyen, co-founder of Claypot AI. -
Savol:
What benefits does this book offer to machine learning practitioners?
Javob: The book provides a framework for making informed design decisions, automating model processes, and ensuring responsible AI development.
Chip Huyen , Özgür Kaya (Çevirmen) Editorial Review
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Pros
- Good foundational knowledge
- Clear, structured approach
- Useful real-world examples
- Engaging writing style
- Covers essential concepts
Kamchiliklari
- Some sections may lack depth.
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Xususiyatlar va Afzalliklar
- Learn a holistic approach to designing reliable machine learning systems.
- Make informed design decisions through real case studies.
- Automate the continuous development and updating of models.
- Develop effective monitoring systems for production problems.
- Create a flexible machine learning platform adaptable to various scenarios.
- Build responsible and sustainable machine learning systems.
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