Explainable AI for Practitioners

Designing and Implementing Explainable ML Solutions

By: Michael Munn, David Pitman

Write A Review

eText | 31 October 2022 | Edition Number 1

At a Glance

Format
ePUB

eText

$75.89

or 4 interest-free payments of $18.97 with

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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