Interview with Aditya Bhattacharya

Aditya Bhattacharya is the author of Applied Machine Learning Explainability Techniques; we got the chance to sit down with him and find out more about his experience of writing with Packt.

Q: What are your specialist tech areas?
Aditya: Data science; machine learning; deep learning; explainable AI; artificial intelligence; software engineering; Python

Q: How did you become an author for Packt? Tell us about your journey. What was your motivation for writing this book?
Aditya: I am currently working as an Explainable AI Researcher at KU Leuven, Belgium. So, my book on explainable AI is based on my research in the field. I also want to contribute more for the community and help towards bridging the gap between AI and end-users. So, that is why I am passionate about the field of explainable AI. This was the biggest motivation for me for writing the book.
Luckily, I was scouted by Abhinaba Chakraborty from Packt where he introduced me for the opportunity of being an author with Packt. I was interested and this is how I became an author for Packt! The journey has been amazing so far as the entire team of Packt is very supportive and provided me with the necessary support to complete this book on Applied Machine Learning Explainability Techniques.

Q: What kind of research did you do, and how long did you spend researching before beginning the book?
Aditya: I was already working as an Explainable AI Researcher and had delivered some talks on the same topic. So, I already was familiar with the topic. But I did go through recent research papers and publications and even videos and projects on GitHub.
For research, I did spend around 40-50 hours in researching before beginning the book.

Q: Did you face any challenges during the writing process? How did you overcome them?
Aditya: No challenges as such, but a lot of learning was involved. I did have to sacrifice many weekends and a tremendous amount of family time!

Q: What’s your take on the technologies discussed in the book? Where do you see these technologies heading in the future?
Aditya: Explainable AI is the future of AI! It is extremely essential for bringing AI closer to end-users and increase AI adoption by making AI transparent and trustworthy!

Q: Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?
Aditya: This book is a unique blend of conceptual understanding, practical problem solving and applying the gained knowledge for industrial and academic use-cases. Currently, similar books which are available either consider academic perspective or semi-industrial perspective, but not both. Hence, this is a major USP for this book. It kind of covers almost A to Z in Explainable AI!

Q: What are the key takeaways you want readers to come away with from the book?
Aditya: The following are the three main takeaways for the readers:
– Learn about various explainability methods for designing robust and scalable explainable ML systems.
– Learn to apply XAI frameworks like LIME, SHAP & others to make ML models explainable for practical problems.
– Design user centric explainable ML systems using the guidelines provided for industrial applications.

Q. What advice would you give to readers learning tech? Do you have any top tips?
Aditya: Since the field of technology is rapidly evolving, my advice would be to keep yourself updated with the latest technology trends and read more books, research literatures and articles and blog posts! Having a product mindset helps in applying technology for creating bigger impact!

Q. Do you have a blog that readers can follow?
Aditya: Yes I do. I have my own website: and I write on medium:

Q. Can you share any blogs, websites and forums to help readers gain a holistic view of the tech they are learning?
Aditya: I think YouTube, Medium and GitHub are the best platforms for learning any technology!

Q. How would you describe your author journey with Packt? Would you recommend Packt to aspiring authors?
It was a delightful experience! I would recommend doing a thorough research and preparing a robust writing plan before beginning the book. Before every chapter, I used to plan the outline, keep all notes, topics, code pieces, images ready. Then I used to start writing. I think with this approach I was able to complete the submission process on the planned schedule.

Q. Do you belong to any tech community groups?
Yes, I do. Apart from few tech community groups related to AI, ML and Data Science on LinkedIn and Facebook, I am part of a Research community called MUST Research.

Q. What are your favorite tech journals? How do you keep yourself up to date on tech?
I read a lot on Medium, Towards Data Science and Paper with Code.

Q. How did you organize, plan, and prioritize your work and write the book?
I usually follow the old fashion way of keeping a diary and noting everything there. I also use simple excel sheets for high-level planning. It is cliché but still works :)!

Q. What is that one writing tip that you found most crucial and would like to share with aspiring authors?
I would appropriate explanations through images and visuals is the best way to attract reader’s attention.

You can find Aditya’s book on Amazon by following this link : Please click here

Applied Machine Learning Explainability Techniques – Available on Amazon