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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
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
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
artikal br.: 51479271

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

87% of respondents would recommend this to a friend

artikal br.: 51479271

RSD 6349

RSD 10090

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What Stands Out

Iterative Approach
Emphasizes an iterative process that allows developers to refine machine learning models continuously, ensuring more robust and scalable applications suitable for production environments.
Practical Guidance
Offers step-by-step guidance and real-world scenarios that help practitioners effectively apply machine learning principles, bridging the gap between theory and practical implementation.
Comprehensive Coverage
Covers a wide range of topics in machine learning system design, making it a valuable resource for both novices and experienced professionals looking to enhance their skill set.

Детаљи о производу

Explore the iterative process of building production-ready machine learning applications. Get the 1st Edition now at Ubuy KW, your Serbia for all your shopping needs.
  • 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 problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems
Publisher O'Reilly Media
Publication date June 21, 2022
Edition 1st
Language English
Print length 386 pages
ISBN-10 1098107969
ISBN-13 978-1098107963
Item Weight 1.4 pounds (640 grams)
Dimensions 6.9 x 0.7 x 9.1 inches (17.5 x 1.8 x 23.1 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists seeking practical frameworks for developing and deploying scalable machine learning systems effectively.

  • Software Engineers

    Provides software engineers with guidelines for integrating machine learning into existing applications and enhancing production readiness.

  • Project Managers

    Useful for project managers overseeing machine learning projects, ensuring alignment between development and operational goals.

Not Suitable For
  • Complete Beginners

    Not suitable for total newcomers; prior knowledge of machine learning principles is necessary to grasp the content.

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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications is authored by Chip Huyen and published by O'Reilly Media. This book serves as an essential guide for practitioners aiming to transition machine learning from research to real-world applications. Its focus on iterative processes, along with a clear writing style, allows readers to grasp complex concepts surrounding data-centric AI, model deployment, and monitoring efficiently. Featuring real-world case studies and practical examples, Huyen provides insights into productionizing ML systems that are valuable for engineers, researchers, and data scientists alike. The structured approach makes this resource particularly beneficial for those mentoring others in the field.

Customer Reviews & Ratings

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1003 recenzije kupaca
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    2%
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Прос

  • Comprehensive overview of machine learning implementation
  • Clear explanations of complex concepts
  • Real-world case studies make learning relatable
  • Great resource for mentoring undergrad students
  • Structured for both practitioners and leaders

Цонс

  • Some topics may not delve deeply into details

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RSD 6349

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Naručite sada i dobijte oko Petak, Novembar 13

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