The Machine Learning Workflow

A machine learning workflow is a structured process that guides how we move from a raw dataset to a deployed, working model.

Following a clear workflow ensures efficiency, reproducibility, and better results.

Rather than listing each stage here, take a look at the whiteboard diagram for a visual breakdown of the workflow steps and their relationships.

• A well-structured ML workflow reduces errors and improves reproducibility.
• The steps are iterative — you might return to earlier stages if performance isn't satisfactory.
• Scikit-learn provides tools for almost every stage, from preprocessing to evaluation.