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.
Key Takeaways
- 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.
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