Category: Tutorials

Hyperparameter tuning is where data scientists tend to waste the most time. I’ve watched teams spend three weeks tuning a model that had fundamental data problems. They squeezed out a 2% accuracy gain while ignoring that their feature engineering was garbage. In reality, hyperparameter tuning is the last thing you should do, not the first.…

Model deployment is where most ML projects die. I’ve seen data scientists build brilliant models that never leave their Jupyter notebooks. The model works perfectly in development, it just never makes it to production. A model that sits in a notebook is worth exactly zero dollars. Deployment is what turns your ML work into business…