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# 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.

<br/>

## 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*.

The machine learning workflow is iterative, meaning it allows for returning to earlier steps if necessary. This flexibility is crucial for refining and improving models, as new insights often emerge during the process. Iteration enables continuous improvement and adaptation to changes or new data.

### The machine learning workflow involves a one-way process from defining the problem to deploying and monitoring the model.