Aditya Bhattacharya - Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
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- Book:Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
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Key Features
- Explore various explainability methods for designing robust and scalable explainable ML systems
- Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
- Design user-centric explainable ML systems using guidelines provided for industrial applications
Book Description
Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.
Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. Youll begin by gaining a conceptual understanding of XAI and why its so important in AI. Next, youll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, youll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.
By the end of this ML book, youll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.
What you will learn
- Explore various explanation methods and their evaluation criteria
- Learn model explanation methods for structured and unstructured data
- Apply data-centric XAI for practical problem-solving
- Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others
- Discover industrial best practices for explainable ML systems
- Use user-centric XAI to bring AI closer to non-technical end users
- Address open challenges in XAI using the recommended guidelines
Who this book is for
This book is designed for scientists, researchers, engineers, architects, and managers who are actively engaged in the field of Machine Learning and related areas. In general, anyone who is interested in problem-solving using AI would be benefited from this book. The readers are recommended to have a foundational knowledge of Python, Machine Learning, Deep Learning, and Data Science. This book is ideal for readers who are working in the following roles:
- Data and AI Scientists
- AI/ML Engineers
- AI/ML Product Managers
- AI Product Owners
- AI/ML Researchers
- User experience and HCI Researchers
Table of Contents
- Foundational Concepts of Explainability Techniques
- Model Explainability Methods
- Data-Centric Approaches
- LIME for Model Interpretability
- Practical Exposure to Using LIME in ML
- Model Interpretability Using SHAP
- Practical Exposure to Using SHAP in ML
- Human-Friendly Explanations with TCAV
- Other Popular XAI Frameworks
- XAI Industry Best Practices
- End User-Centered Artificial Intelligence
Aditya Bhattacharya: author's other books
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Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
Aditya Bhattacharya
BIRMINGHAMMUMBAI
Copyright 2022 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Product Manager: Dinesh Chaudhary
Senior Editor: Tazeen Shaikh
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First published: July 2022
Production reference: 2010722
Published by Packt Publishing Ltd.
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ISBN 978-1-80324-615-4
www.packt.com
To my lovely wife, Shreya. Thank you for being my eternal partner of our fairy tale called Life!
To my mother, Banani, and my father, Asit. Thank you for all your support, sacrifices and for teaching me the best lessons of life!
To my wonderful sister, Anuradha. Thank you for being my greatest advocate forever!
Aditya Bhattacharya
Aditya Bhattacharya is an explainable AI researcher at KU Leuven with 7 years of experience in data science, machine learning, IoT, and software engineering. Prior to his current role, Aditya worked in various roles in organizations such as West Pharma, Microsoft, and Intel to democratize AI adoption for industrial solutions. As the AI lead at West Pharma, he contributed to forming the AI Center of Excellence, managing and leading a global team of 10+ members focused on building AI products. He also holds a master's degree from Georgia Tech in computer science with machine learning and a bachelor's degree from VIT University in ECE. Aditya is passionate about bringing AI closer to end users through his various initiatives for the AI community.
I am immensely grateful to all who have been close to me and have supported me throughout the journey of writing this book, especially my wife Shreya, parents, sister, and all my aunts. A special thanks to my colleagues at the Augment research group of KU Leuven. I am grateful to Dr. Katrien Verbert for giving me the opportunity to pursue my journey as an XAI researcher. A big shoutout to all the reviewers for helping me throughout the writing process. Last, but not least, thanks to Abhijit Jana, who has always inspired me to step outside my comfort zone and pursue bigger challenges in life.
Sumedh VilasDatar is a machine learning engineer with 6 years of work experience in the field of deep learning, machine learning, and software engineering. He has a proven track record of single-handedly delivering end-to-end engineering solutions to real-world problems. He works at the intersection of engineering and products and has developed deep learning products from scratch that have been used by a lot of customers. Currently, Sumedh works in R&D on applied deep learning and has several granted patents and several more applied for. Sumedh studied biomedical engineering focused on computer vision and then went on to pursue a master's in computer science focused on AI.
Abhijit Jana is a trusted technology leader and advisor with 15 years of experience in the IT industry, with expertise in development, architecting, engineering, consulting, service delivery, and leadership. He is currently associated with West Pharmaceutical Services as a director of software engineering and is responsible for building and leading the software engineering team. Previously, he worked at Microsoft and is a former Microsoft MVP and Code Project MVP, and has been a speaker at various technology conferences. He is the author of the book Kinect for Windows SDK Programming Guide and coauthored the book HoloLens Blueprint. He is also the founder of Daily .NET Tips, a well-known website for developers, architects, and consultants.
Sk Nishan Ali works as a full stack data scientist at UnitedHealth Group. He has 6 years of experience across diverse areas of AI and machine learning, which include computer vision, classical machine learning, and natural language processing, and touches upon several business domains: healthcare, CRM, and sourcing. He has been instrumental in building high-performance end-to-end AI/ML products.
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