Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
Learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments.
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)
Articolo #: 57403947

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback)

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Articolo #: 57403947

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Cosa salta all'occhio

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.

Dettagli del prodotto

Shop Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, (Paperback) online at a best price in Albania. 276053840
  • Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreComputing & Internet
Publication dateJune, 2022
Pages386
Reading levelProfessional and Scholarly
SubgenreData Science
Series titleNo Series
Edition1
PublisherO'Reilly Media
Original languagesEnglish
LanguageEnglish
Is collectibleN
EditorVaishali V Phalke, Mohannad Ibrahim, Douglas J Quint, Hemant Parmar, Gaurang Shah, Sachin Gujar
Recording time0 min
Retail packagingSingle 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 weight1.35 lb (610 grams)
Bisac subject headingComputers

A chi è consigliato?

Suitable For
  • 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.

Not Suitable For
  • Beginners in ML

    May be too advanced for those without prior knowledge of machine learning or programming concepts.

DESCRIZIONE DEL PRODOTTO

Hai qualche domanda? chatta con noi

Domande e risposte dei clienti

  • Domanda: What is the primary focus of this book?

    Risposta: The book focuses on designing reliable, scalable, and maintainable machine learning systems adapted to changing environments.
  • Domanda: Are there real-life examples included in the book?

    Risposta: Yes, the book includes actual case studies to illustrate the iterative design framework.
  • Domanda: Who is the author of this book?

    Risposta: The author is Chip Huyen, co-founder of Claypot AI.

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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Vantaggi

  • 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

Contro

  • May not suit casual readers or beginners

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Caratteristiche e benefici

  • 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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