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Machine Learning System Design: With end-to-end examples
Machine Learning System Design: With end-to-end examples
Machine Learning System Design: With end-to-end examples
Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.
Machine Learning System Design: With end-to-end examples
منتج #: 100635161

Machine Learning System Design: With end-to-end examples

منتج #: 100635161

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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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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.

تفاصيل المنتج

Shop Machine Learning System Design: With end-to-end examples online at a best price in Libya. 1633438759
  • Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity.In Machine Learning System Design: With end-to-end examples you will learn:The big picture of machine learning system designAnalyzing a problem space to identify the optimal ML solutionAce ML system design interviewsSelecting appropriate metrics and evaluation criteriaPrioritizing tasks at different stages of ML system designSolving dataset-related problems with data gathering, error analysis, and feature engineeringRecognizing common pitfalls in ML system developmentDesigning ML systems to be lean, maintainable, and extensible over timeAuthors Valeri Babushkin and Arseny Kravchenko have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system.About the technologyDesigning and delivering a machine learning system is an intricate multistep process that requires many skills and roles. Whether you’re an engineer adding machine learning to an existing application or designing a ML system from the ground up, you need to navigate massive datasets and streams, lock down testing and deployment requirements, and master the unique complexities of putting ML models into production. That’s where this book comes in.About the bookMachine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips.What's insideMetrics and evaluation criteriaSolve common dataset problemsCommon pitfalls in ML system developmentML system design interview tipsAbout the readerFor readers who know the basics of software engineering and machine learning. Examples in Python.About the authorValerii Babushkin is an accomplished data science leader with extensive experience. He currently serves as a Senior Principal at BP. Arseny Kravchenko is a seasoned ML engineer currently working as a Senior Staff Machine Learning Engineer at Instrumental.Table of ContentsPart 11 Essentials of machine learning system design2 Is there a problem?3 Preliminary research4 Design documentPart 25 Loss functions and metrics6 Gathering datasets7 Validation schemas8 Baseline solutionPart 39 Error analysis10 Training pipelines11 Features and feature engineering12 Measuring and reporting resultsPart 413 Integration14 Monitoring and reliability15 Serving and inference optimization16 Ownership and maintenance
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)

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Suitable For
  • 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.

  • AI Enthusiasts

    Great for individuals passionate about AI who want to understand comprehensive system design through real-world examples.

Not Suitable For
  • Beginner Programmers

    Not suitable for users with limited programming experience who may struggle with advanced concepts and examples presented.

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أسئلة العملاء & الإجابات

  • سؤال: What skills do I need to benefit from this book?

    إجابه: A basic understanding of software engineering and machine learning is required.
  • سؤال: What formats does the book offer?

    إجابه: The print book purchase includes free eBook versions in PDF and ePub formats.
  • سؤال: Who are the authors of this book?

    إجابه: The authors are Valerii Babushkin and Arseny Kravchenko, both experienced professionals in data science and machine learning.

Data Modeling & Design Editorial Review

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إيجابيات

  • Clear explanations of concepts
  • Practical end-to-end examples
  • Comprehensive coverage of topics
  • Suitable for various skill levels

سلبيات

  • Some sections could use more depth.

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