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Item #:
254555492
Machine Learning Engineering in Action, (Paperback)
Item #:
254555492
XCD 217
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Product Details
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | April, 2022 |
| Pages | 576 |
| Reading level | Professional & Vocational |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 1 |
| Publisher | Manning Publications |
| Original languages | English |
| Language | English |
| Edu focus | Engineering |
| Is collectible | N |
| Digital file format | PDF, Kindle, ePub |
| Digital reader format | PDF, Kindle, ePub |
| Digital audio file format | PDF, Kindle, ePub |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.42 x 1.08 x 9.32 in (18.8 x 2.7 x 23.7 cm) |
| Assembled product weight | 2.09 lb (950 grams) |
| Bisac subject heading | Computers |
Product Description
Machine Learning Engineering in Action, (Paperback)
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Ben Wilson All Books Editorial Review
Machine Learning Engineering in Action, a paperback book published by Manning Publications, serves as an essential resource for professionals in computing and internet fields. With 576 pages of insightful content, this non-fiction book explores the intricacies of data science and machine learning engineering, making it an ideal read for those seeking to deepen their knowledge in this area. The publication is designed for a professional and vocational reading level, providing readers with practical insights and methodologies relevant in today's tech landscape.
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Pros
- Comprehensive coverage of machine learning engineering
- Ideal for professional and vocational readers
- Well-structured content for easy understanding
- Includes diverse topics within data science
- Published by a reputable publisher
Cons
- No collectible edition available
Product Price History
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XCD 217
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Features & Benefits
- Learn field-tested techniques for successful machine learning projects.
- Understand data evaluation and project scoping for effective solutions.
- Implement Agile methodologies for faster prototyping and stakeholder communication.
- Adopt software engineering standards for better code management and testing.
- Receive practical advice from industry expert Ben Wilson for deploying resilient ML systems.
- Access friendly, peer-to-peer explanations illustrated with production-ready source code.
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