Model Training

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.

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.

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.

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.