ML Debugging Workshop

​A workshop for machine learning practitioners looking to sharpen their debugging skills and make ML development smoother.

​​We’ll cover:
🔍 Why ML debugging is so much harder than regular debugging
🛠️ A solid process you can follow when debugging ML systems
🧰 How to design workflows that lead to fewer bugs in the first place
🔁 How to reflect after bugs to keep improving
… and more

We’ll also debug some real cases together to illustrate key points. It’ll be interactive, fast-paced, and you’ll be expected to participate 🧑‍💻

Who is this for?
This workshop is not for people just getting started in ML. It’s for practitioners who’ve already developed ML systems. Ideally, those who’ve been burned by bugs before and want to improve how they deal with them.

A few more details:
✔️ Model-agnostic (XGBoost, CNNs, Transformers—you name it)
✔️ Framework-agnostic (PyTorch, TensorFlow, whatever)
❌ Not language-agnostic—we’ll use Python 😛
🎯 Focused on local dev workflows (pre-production stage)

​​URL to join will be released closer to the event.