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Five Recommendations from a Leapfrog ML Engineer


Saurav Dhungana is a Principal Data Science and AI Engineer at Leapfrog. He enjoys building systems with AI algorithms that create real-world business impact. When he’s not working on his next big project, he likes watching movies and cycling around the hills of Kathmandu.  

The internet can be a powerful pool of information if you know where to look. But sometimes, it’s hard to cut through the noise and find the right inspiration. So, I’ve compiled a list of recommendations for you to check out, especially if you are an aspiring ML/AI engineer. 

A podcast to listen to

The TWIML AI Podcast (This Week in Machine Learning and AI Podcast)

This is a long-running podcast that I’ve been following for many years. Sam Charrington is an excellent and knowledgeable interviewer, and the sheer breadth of topics within AI/ML he covers has broadened my own horizons in the subject. 

A YouTube to watch

A Chat with Andrew on MLOps: From Model-centric to Data-centric AI

This was a tough choice given all the videos out there. I’m choosing this one from Professor Andrew Ng because of the impact I think it’ll have on how we think about working as ML Engineers. The field has been, until recently, dominated by this model-centric way of thinking. This talk has put the need for having better quality data back into the general discourse, which I’ve always believed and advocated for.

A course worth taking

Structure and Interpretation of Computer Programs

This is not an ML course per se but about writing computer programs and quite an old one from MIT. But I think SICP is the most important book on computer science ever written, and this course is from the original authors. The problem-solving skills this gives you will make you a better engineer regardless of the programming language or tools you use.   

An app to check out

Obsidian.md

This is an app I’ve started using recently. I’m generally working on a few things at the same time and constantly take notes. Obsidian helps me organize and connect related notes in a powerful and visual way. This has made recalling and finding them in the future a lot easier and has improved my productivity. 

A book to read 

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

This book was given to me as a gift. The author, Nate Silver, is someone I have admired for a long time. He built his career at the NY Times and is best known for using data to predict several US election results accurately. The knowledge he imparts here about extracting the true signals from noisy data is invaluable and a must-read for every data science practitioner.

Final Words

These are some of my favorite go-to resources for when I feel uninspired or stuck. Some of them keep me grounded as an ML engineer, while others improve my productivity at work. 

Thank you for your time! Stay tuned for more recommendations from other amazing Leapfroggers! ⏰

If you want to see more of Saurav Dhungana’s work, check out his GitHub profile.

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