Model training and tracking

Learn how to create a model training job, work with the input data and the model artifacts, use an MLRun function to automate the tracking, use a function from the MLRun hub to train the model, to optimize hyperparameter tuning, and to use the feature store to train your model.

In this section

• Create a basic training job
• Working with data and model artifacts
• Automated experiment tracking
• Using the built-in training function
• Hyperparameter tuning optimization