Here we discuss running the training pipeline for on-cloud implementation. On-premise implementation is almost identical and the difference would highlighted for each of the components. In order to create a training pipeline click on the create training pipeline and you will be proceded to the first step – “Pipeline details”.
In this section the following fields are filled by the user. Please note that none of them are mandatory. The name of the pipeline and the description would be displayed in the pipeline run page. The git username and git token gives access to your code to Robotika. You can skip the git credentials and directly upload the files when configuring the components. The organization name is fixed and cannot be modified.
Pipeline component config
For the pipeline to run the following components should be configured:
The components that are not configured on this step but contains valuable information during the debugging and after the deployment are:
Please refer to the page corresponding to the specific component for more details.
The pipeline scheduler is responsible for the retraining of ML pipeline. Currently Robotika supports only the retraining based on the time schedule. Users can choose the periodicity of the retraining. Please note the the first Training occurs as soon as user clicks a button “Save”.