ML Workflow and Model Lifecycle

Every machine learning project follows a structured lifecycle — moving from defining the problem to deploying and maintaining a working model.

The workflow includes:

• Defining the problem
• Preparing the data
• Training and evaluating the model
• Deploying and monitoring the model

A visual breakdown of each stage is provided in the slide deck for this lesson.

• The ML lifecycle is iterative, not linear — you’ll often revisit earlier steps to refine performance.
• Scikit-learn supports every stage — from data preparation and training to evaluation and model tuning.