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Practice

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.


Key Takeaways​

  • 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.

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