Guanhua Wang is the author of Distributed Machine Learning with Python. 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 area(s)?
Guanhua: Distributed Machine Learning, Distributed Systems, Deep Learning.
Q: How did you become an author for Packt? Tell us about your journey. What was your motivation for writing this book?
Guanhua: A year ago, I was contacted by one of your people in Acquisitions team. He asked me to author a book on distributed machine learning, which is the main focus of my Phd research area. I am happy to let people learn more about my research domain.
Q: What kind of research did you do, and how long did you spend researching before beginning the book?
Guanhua: I am a computer science PhD student at UC Berkeley. My main research focus is distributed machine learning systems. I have been working in this domain for more than 6 years.
Q: Did you face any challenges during the writing process? How did you overcome them?
Guanhua: Yes, it takes a lot of time to write each chapter. As a PhD student who already got a lot of tasks to do, I just use my spare time during the weekends to finish this book.
Q. What’s your take on the technologies discussed in the book? Where do you see these technologies heading in the future?
Guanhua: As deep learning model grows larger and larger, distributed model training and serving is a must-have. Therefore, the technology discussed in my book can help people scaling out their model training and serving more efficiently.
Q: Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?
Guanhua : I don’t see many books specifically on Distributed machine learning systems. Thus I believe not too many competitors in this market. ML systems just become more and more popular in past few years. And I believe this is the right time for more people to learn the basic concepts and schemes in distributed ML systems domain.
Q. What are the key takeaways you want readers to come away from the book with?
Guanhua: After go through the book, readers can do distributed model training and serving in an efficient way.
Q. What advice would you give to readers learning tech? Do you have any top tips?
Guanhua: Before reading, you need to have basic knowledge about machine learning concepts, popular deep neural networks. The readers should also have experience in ML model training and serving using single GPU.
Q. Can you share any blogs, websites, and forums to help readers gain a holistic view of the tech they are learning?
Guanhua: People can read my previous publications on my google scholar.
Q. How would you describe your author journey with Packt? Would you recommend Packt to aspiring authors?
Guanhua: Pretty intense but interesting.
Q. Do you belong to any tech community groups?
Guanhua: I am just a student, right now I don’t belong to any tech groups.
Q. What are your favorite tech journals? How do you keep yourself up to date on tech?
Guanhua: MLSys Conference every year.
Q. How did you organize, plan, and prioritize your work and write the book?
Guanhua: Write over weekends.
Q. What is the one writing tip that you found most crucial and would like to share with aspiring authors?
Guanhua: Try your best to meet the deadline.
You can find Guanhua’s book on Amazon by following this link: Please click here.