ML Workflow and Model Lifecycle
A machine learning project typically progresses through several stages, from understanding the problem to deploying and monitoring the model.
The workflow includes:
- Defining the problem
- Preparing the data
- Training and evaluating the model
- Deploying and monitoring the model
You can find a detailed breakdown of these stages in the slide deck for this lesson.
Key Points
- The lifecycle is iterative, not one-way — you may return to earlier steps.
- Scikit-learn provides tools for data preparation, training, evaluation, and more.
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