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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
Item #: 51479271

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

Item #: 51479271

NZD 97

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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 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.

Product Details

Explore the iterative process of building production-ready machine learning applications. Get the 1st Edition now at Ubuy KW, your New Zealand for all your shopping needs.
  • 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
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.

Product Description

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

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Intelligence & Semantics Editorial Review

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.

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Pros

  • 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

Cons

  • Some topics may not delve deeply into details

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