Machine Learning Engineer

The journey

1. What you learn

   1. The mathematics and mechanics underneath modern machine learning
   2. Models built, trained, and evaluated on problems that warrant them
   3. The engineering that gets a model out of a notebook and into production
   4. Honest evaluation, including the failures a headline metric hides

2. What you will understand

   1. Feature engineering as the work that usually decides model quality
   2. Training, validation, and the leakage that quietly invalidates both
   3. Why a strong offline score can mean nothing once real traffic arrives
   4. Models as products, with everything that implies about maintenance

3. What you will build

   1. A trained model with a validation story that holds up under challenge
   2. A served model with monitoring that shows when its performance drifts
4. 1. The proof you build

      Evidence that you can build a model that works on real data and keeps working after it ships.

      See how proof works