To get the most out of this book

To get the most out of this book, it is important to understand the context and target audience. This book is focused on responsible AI and machine learning model governance, providing in-depth coverage of key concepts such as explainable and ethical AI, bias in AI systems, model interpretability, model governance and compliance, fairness and accountability in AI, data governance, upskilling, and education for ethical AI. The target audience includes data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for building and deploying AI models in their organizations.

To maximize the benefits of this book, you should have a basic understanding of machine learning and AI. It is recommended to read the chapters in order to build a comprehensive understanding of the topics covered. Additionally, the hands-on examples and practical guidance provided in the book can be applied to real-world situations and can be used as a reference for future projects.

We sincerely hope you enjoy reading this book as much as we enjoyed writing it.

Software/hardware covered in the book	Operating system requirements
Jupyter Notebook (Python 3.x)	Windows, macOS, or Linux

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

This book is filled with references to the classic science fiction novel, The Hitchhiker’s Guide to the Galaxy, one of my favorite books of all time. So, excuse the puns and whimsical language as I pay homage to the humor and creativity of Douglas Adams. May this book guide you on your own journey through the world of AI and machine learning, just as the Guide guided Arthur Dent on his interstellar adventures.