Designing Machine Learning Systems: An Iterative Process for Production-ready Applications
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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
Articolo #: 216097779

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

Articolo #: 216097779

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Cosa salta all'occhio

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.

Dettagli del prodotto

Shop Designing Machine Learning Systems: An Iterative Process for Production-ready Applications online at a best price in San Marino. 1098107969
  • Written by experts from O'Reilly, a leading publisher in technology and business
  • Designed for individuals who want to leverage machine learning to solve real-world problems
  • Caters to ML engineers, data scientists, data engineers, ML platform engineers, and engineering managers
  • Addresses scenarios such as deploying and updating models, automation, bias detection, and ML system responsibility
  • Also beneficial for tool developers, individuals seeking ML-related roles, and technical and business leaders
  • Assumes basic understanding of various ML models, techniques, metrics, statistical concepts, and common ML tasks
PublishersO'Reilly Media
Date of publicationMay 31, 2022
Prints1.
LanguageEnglish
Print Length360 pages
ISBN-101098107969
ISBN-13978-1098107963
Dimensions17.53 x 1.78 x 23.11 cm

A chi è consigliato?

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.

DESCRIZIONE DEL PRODOTTO

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

Hai qualche domanda? chatta con noi

Domande e risposte dei clienti

  • Domanda: Who is the author of this book?

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

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

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

İngilizce Baskı Chip Huyen Format: Kağıt Kapak Editorial Review

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Vantaggi

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

Contro

  • Some examples may be too specific.

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€ 67

Ordina subito e ricevilo entro Martedì, Agosto 18

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Caratteristiche e benefici

  • Design ML systems that are reliable and adaptable
  • Learn to process and create training data
  • Automate the process for continually developing, evaluating, deploying, and updating models
  • Develop a monitoring system to detect and address production issues
  • Architect an ML platform that serves across use cases
  • Develop responsible ML systems

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