Once you’ve optimized your code, data, and model workflow, the next step is to automate the process. Dkube provides several flexible mechanisms for automating your workflow.
DKube supports Kubeflow Pipelines natively. This provides a graphical method to view and execute a predefined set of steps. These can be setup, preprocessing, training, serving, or any action necessary to achieve the required results. The pipeline can take inputs to use for the execution.
The automation can even be triggered when the GitHub repo is updated. A set of steps can be executed when the code is changed, which can build an image, start a job, or run a pipeline.