Formal training involves a significant number of iterations. This creates a logistical challenge just to keep track of what inputs lead to what outputs. DKube enhances the standard Kubeflow tracking ability with a powerful, automatic, built-in version control system for datasets and models.
The full lineage of the model is shown graphically. This helps the user track and understand what changes impact preprocessing or training. DKube includes a metric collection, display, and comparison capability based on MLFlow, and the lineage information provides insight into the reasons behind the model metrics.
Later, the lineage can be used to reproduce or audit the model, and to identify what might be causing issues with the production serving outcomes.
In addition, DKube keeps track of what code and dataset repositories are used in the training. This provides a quick indication of how broadly the input components are being used.