The Machine Learning Workflow

A machine learning workflow is a structured process that outlines how raw data is transformed into a trained and deployed model.

Following a defined workflow helps maintain efficiency, reproducibility, and consistency across projects.

Instead of listing the stages here, review the whiteboard diagram to see how each step connects within the overall pipeline.

• A clear ML workflow reduces errors and improves reproducibility.
• The process is iterative — you often revisit earlier steps to refine performance.
• Scikit-learn supports nearly every stage, from data preprocessing to model evaluation.