model_training.qmd

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title: Model Training
order: 3
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# Model Training

The **Image ML Pod** framework provides a pipeline titled `model_training` with placeholder functions for training and evaluating models. This pipeline is designed to be modular and easily customizable for various use cases.

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## Key Features

1. **Training and Evaluation**  
   The pipeline includes templates for implementing model training and evaluation logic. You can modify these functions to fit your specific requirements.

2. **Model Tracking with MLflow**  
   We use **MLflow** to log:
   - Models
   - Hyperparameters
   - Metrics  

   MLflow provides a convenient way to track experiments, making it easier to compare different models and their performance over time.

3. **Model Selection**  
   As an example, the final node in the pipeline iterates through all the models trained so far and identifies the model with the best performance. This helps streamline the model selection process.

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## Benefits of MLflow

- **Experiment Tracking**: Easily compare models based on metrics, configurations, and outputs.  
- **Reproducibility**: Logs ensure that model training processes can be replicated accurately.  
- **Deployment Ready**: Models logged with MLflow can be directly deployed using MLflow’s deployment tools.

For more details on how to use MLflow, refer to the [MLflow Documentation](https://mlflow.org/docs/latest/index.html).

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## Next Steps

To get started, modify the placeholder functions in the `model_training` pipeline to include your model-specific training logic. Leverage MLflow to monitor your experiments and ensure you select the best-performing model for deployment or further analysis.