This is the 10th episode of the podcast and a really special one. I've got the authors of the Practical Natural Language Processing book. The book is a comprehensive guide to building, iterating and scaling real world NLP Systems. It is for anyone who is involved in any way in building NLP systems in industry - from software engineers to data scientists to ML engineers to product managers and business leaders. The book is already topping the charts on Amazon and has been endorsed by various experts from academia and industry.
So for this episode, I talk to the authors of the book - Sowmya, Bodhi, Anuj and Harshit.
We talk about the key ideas behind the book - about how it bridges the gap between theory and building practical ML/NLP solutions. We discuss the inspiration behind writing the book, how it stands out, how it has been structured, who can benefit from it and lots more. We also talk about the elephant in the room, GPT-3 and try to make sense of the hype around it and understand it's broader impact and how it positions us, as a community to leverage these systems on a wider scale.
We also talk about the state of ML and NLP in general, about the many misconceptions and misinformed expectations that surround these fields in the context of the business of AI, and about how they've tried to incorporate this message in the book.
Authors / Guests:
Sowmya Vajjala is a research officer at National Research Council, Canada’s largest federal research and development organization. Her past work experience spans both academia as a faculty at Iowa State University, USA and industry at Microsoft Research.
Bodhisattwa Majumder is a Computer Science PhD student working on NLP and ML at UC San Diego. His research interests include Lang Generation and Dialogue & Interactive Systems
Anuj Gupta is currently Head of Machine Learning and Data Science at Vahan Inc. He has built NLP and ML systems at Fortune 100 companies as well as startups as a senior leader.
Harshit Surana is a co-founder at DeepFlux Inc. He has built and scaled ML systems and engineering pipelines at several Silicon Valley startups as a founder and an advisor.