Thanks for reaching out to us, you may want to consider Azure Event Grid to see if that can help your model version control, a general idea to do so as below for your reference -
- Register your pipeline as first step: First, you need to create and register your pipeline in Azure Machine Learning. This involves defining the steps of your pipeline and registering it with AzureML.
- Set up model monitoring or versioning: You need a way to detect when a model is updated or a new version is deployed. This can be done through model monitoring or versioning mechanisms provided by AzureML.
- Create an event trigger: Set up an event trigger that listens for changes to the model or its deployment. Azure Event Grid can be used for this purpose. When a model update event is detected, it will trigger an event.
- Subscribe to the event: Subscribe to the event triggered by the model update. You can do this programmatically using Azure Event Grid SDK or through the Azure portal.
- Trigger the pipeline: Once the event is received, you can trigger the registered pipeline using AzureML SDK or REST API. Pass any necessary parameters to the pipeline if required.
Please let us know if this way can help.
Regards,
Yutong
-Please kindly accept the answer if you feel helpful to support the community, thanks a lot.