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Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions

Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions

Explainable AI for Practitioners: Designing and Implementing Explainable ML
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Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions Paperback - 2022

by Munn, Michael,Pitman, David

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  • Title Explainable AI for Practitioners: Designing and Implementing Explainable ML Solutions
  • Author Munn, Michael,Pitman, David
  • Binding Paperback
  • Condition New
  • Pages 276
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2022-12-06
  • Features Bibliography, Index
  • Bookseller's Inventory # 44547021-n
  • ISBN 9781098119133 / 1098119134
  • Weight 1.05 lbs (0.48 kg)
  • Dimensions 9.1 x 6.9 x 0.8 in (23.11 x 17.53 x 2.03 cm)
  • Category Computers - General Information
  • Library of Congress subjects Machine learning
  • Dewey Decimal Code 006.31
  • Quantity available 5

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From the publisher

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.

Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow.

This essential book provides:

  • A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs
  • Tips and best practices for implementing these techniques
  • A guide to interacting with explainability and how to avoid common pitfalls
  • The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems
  • Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data
  • Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace

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