Hello Model
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Lesson 1 of 8

The ML workflow at a glance

In plain words: Every model project follows the same loop: define, collect, prepare, train, evaluate, deploy, monitor.
  • Define the problem and pick one success metric before touching data.
  • Collect & label real examples that look like what the model will see in production.
  • Prepare the data and lock away a test set.
  • Train a simple baseline first, then something better.
  • Evaluate on data the model has never seen, and read its mistakes.
  • Deploy the simplest way that meets your speed needs, then monitor for data drift.
Tip Most of the effort goes into data and evaluation, not the model itself.