The Challenges of Deploying Simple ML Models: A Practical Guide
There is an old meme showing what Python can do in the hands of Python developers. Deploying ML with Python often looks like that, which can be quite challenging to implement, maintain, and debug once it fails. If you ever had problems with ML model deployment you should own some version of this Frankenstein: In this article, I’m exploring some underlying reasons for that, and trying to answer few related questions:...
Rethinking ML Education: When Project-Based Learning Wins
Ever thought traditional ML classes were boring? I had that thought many times when I was studying. Now as a teacher, I’m searching for a way to make it more engaging. I really believe in hands-on education (make things, break things), so this year I designed “Building ML-powered applications” class for Harbour.Space. As the class is now over, I’ve collected my reflections on how to prepare for such class, how to teach it and when you should really prefer it to other options....
Designing “Building ML-powered applications” class
Hey! I’m searching for a ML-based project we can build during this class that comes from an early startup or non-profit. If you’re working on a project that can be a good fit, let me know! ...
Working on Real Projects to Teach Machine Learning
This year, I was invited to teach at Harbour.Space, and I couldn’t be happier about it. It feels incredible to be back in a real university environment with eager students. I’ve always enjoyed teaching, and this stint has reminded me of something crucial about learning: working on real projects is the most effective way to do that. When you’re learning through practice, you grasp the subject matter much more efficiently. The trial and error method brings about profound insights, even more so when guided by a knowledgeable mentor....