Hello Model

Training basics

The core ideas behind building a model, one short lesson at a time.

  1. 1 The ML workflow at a glanceEvery model project follows the same loop: define, collect, prepare, train, evaluate, deploy, monitor.
  2. 2 Prompting vs fine-tuning vs training from scratchStart with the cheapest option that could work, and only move up when you have the data and a reason.
  3. 3 Train, validation and test splitsSplit your data so you can check the model on examples it has never seen.
  4. 4 Overfitting and underfittingOverfitting is memorising the training data; underfitting is not learning enough from it.
  5. 5 Hyperparameters that matter mostHyperparameters are the settings you choose before training. A few matter far more than the rest.
  6. 6 CPU vs GPU, and keeping costs downTabular models train fine on a laptop CPU; images, text, audio and LLMs usually need a GPU.
  7. 7 Picking the right metricThe metric should match what a mistake costs in real life.
  8. 8 Reproducibility and MLOpsMake every result repeatable: same data + same code + same settings = same model.