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Stavka #:
57403947
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
90% of respondents would recommend this to a friend
Stavka #:
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.
Detalji o proizvodu
| 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.
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Beginners in ML
May be too advanced for those without prior knowledge of machine learning or programming concepts.
OPIS PROIZVODA
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
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Kako kupovati Designing Machine Learning Systems: An Iterative online od Ubuya?
odgovor: Jednostavno je kupovati Designing Machine Learning Systems: An Iterative online s Ubuya.. Samo trebate potražiti proizvod, odabrati način dostave tijekom odjave i dobiti ga na svoju lokaciju. -
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Je li Designing Machine Learning Systems: An Iterative dostupan za online kupnju u Croatia?
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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
Protiv
- May not suit casual readers or beginners
Product Price History
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€ 53
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Značajke i prednosti
- 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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