Skip to main content
Practice

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.


Key Points​

  • 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.

Want to learn more?

Join CodeFriends Plus membership or enroll in a course to start your journey.