Data workflows often begin in notebooks, where experimenting and visualizing results is convenient. But once a workflow needs to run repeatedly or serve other users, it helps to separate data preparation, transformation, model logic, and output handling into reusable components.
A practical project could take a dataset, validate its inputs, calculate useful metrics, and expose the results through a simple API or dashboard. If machine learning is involved, document how the model was evaluated and what its limitations are.
For developers who want to practise the connection between Python engineering and AI, Full-Stack Python For AI Developers is another project-based learning resource to explore.
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How do you prefer to move a data workflow into regular use: a scheduled script, an API, or a dashboard?**
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