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Item #:
100635161
Machine Learning System Design: With end-to-end examples
90% of respondents would recommend this to a friend
Item #:
100635161
MKD 4848
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Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.
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What Stands Out
Comprehensive Examples
Offers detailed end-to-end examples that illustrate practical applications of machine learning, enhancing understanding and usability for practitioners and learners alike.
User-Centric Approach
Focuses on real-world problems, ensuring that readers learn to design systems that effectively address specific user needs and industry requirements.
Expert Insights
Includes expert opinions and best practices, providing readers with the knowledge to avoid common pitfalls and optimize their machine learning system designs.
Product Details
| Publisher | Manning Publications |
| Publication date | February 25, 2025 |
| Language | English |
| Print length | 376 pages |
| ISBN-10 | 1633438759 |
| ISBN-13 | 978-1633438750 |
| Item Weight | 1.4 pounds (640 grams) |
| Dimensions | 7.38 x 0.94 x 9.25 inches (18.7 x 2.4 x 23.5 cm) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for those looking to gain practical insights and hands-on experience in designing machine learning systems.
-
Software Engineers
Beneficial for engineers transitioning to machine learning, needing guidance on integrating ML systems into existing applications.
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AI Enthusiasts
Great for individuals passionate about AI who want to understand comprehensive system design through real-world examples.
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Beginner Programmers
Not suitable for users with limited programming experience who may struggle with advanced concepts and examples presented.
Product Description
Machine Learning System Design: With end-to-end examples
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MKD 4848
Order now and get it around Saturday, October 10
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Features & Benefits
- Step-by-step framework for designing ML systems.
- Includes information gathering, release, and maintenance.
- Real-world examples and requirement checklists provided.
- Covers common pitfalls and dataset-related issues.
- Enhances ML system design interview preparation.
- Filled with practical tips from experienced authors.
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