Designing Machine Learning Systems: An Iterative Process for Production-ready Applications
Designing Machine Learning Systems: An Iterative Process for Production-ready Applications
Learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
Designing Machine Learning Systems: An Iterative Process for Production-ready Applications
Item #: 216097779

Designing Machine Learning Systems: An Iterative Process for Production-ready Applications

Item #: 216097779

AUD 118

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Learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
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What Stands Out

Iterative Framework
Offers a systematic approach to developing machine learning systems, ensuring continuous improvement and adaptability to evolving project needs.
Production-Readiness
Focuses on transforming prototypes into reliable applications, emphasizing practicality and scalability for real-world usage, addressing common deployment challenges.
Comprehensive Guidance
Provides valuable insights and practical tips for practitioners, making it accessible for both beginners and experienced developers aiming for high-quality ML solutions.

Product Details

Shop Designing Machine Learning Systems: An Iterative Process for Production-ready Applications online at a best price in Australia. 1098107969
  • Provides a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements
  • Addresses scenarios such as engineering data, automating model development, developing a monitoring system, architecting an ML platform, and developing responsible ML systems
  • Geared towards ML engineers, data scientists, data engineers, ML platform engineers, and engineering managers
  • Not an introduction to ML, assumes readers have a basic understanding of ML models, techniques, metrics, statistical concepts, and common ML tasks
  • Intended for individuals wanting to leverage ML to solve real-world problems, including technical and business leaders considering adopting ML solutions
  • Includes ample case studies and references, not a tutorial book with extensive code snippets
PublishersO'Reilly Media
Date of publicizationMay 31, 2022
Prints.
Language English
Print length 360 pages
ISBN-10 1098107969
ISBN-13 978-1098107963
Dimensions 17.53 x 1.78 x 23.11 cm

Who Should Buy?

Suitable For
  • Data Scientists

    Perfect for data scientists looking to implement machine learning systems efficiently and iteratively in production environments.

  • Machine Learning Engineers

    Beneficial for engineers who need guidance on building scalable and maintainable machine learning applications.

  • Tech Managers

    Ideal for managers overseeing ML projects needing insights on best practices for deployment and project management.

Not Suitable For
  • Beginners

    Not suitable for complete beginners without prior knowledge in machine learning concepts and techniques.

Product Description

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Customer Questions & Answers

  • Question: Who is the author of this book?

    Answer: The author of this book is Chip Huyen.
  • Question: What are some of the scenarios addressed in this book?

    Answer: The book helps in scenarios such as engineering data, automating processes, developing responsible ML systems, etc.
  • Question: What is the iterative framework used in this book?

    Answer: The iterative framework uses actual case studies backed by ample references.

İngilizce Baskı Chip Huyen Format: Kağıt Kapak Editorial Review

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Pros

  • Clear and practical guidance
  • Focus on real-world applications
  • Detailed iterative process
  • Engaging writing style
  • Suitable for various skill levels

Cons

  • Some examples may be too specific.

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