dstoolkit-mlops-base
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Model deployment with Docker image
The template currently relies on the azureml SDK to natively deploy the model as a real-time webservice in a selected compute, using Model.deploy
. A common request from client is to provide a Docker file that a production team can deploy with a higher degree of flexibility (pod security, management, etc).
The template needs to implement a second scenario which leverages the Model.package
functionality to create a Docker imagefile.
It would be nice to have a parameter in the deploy-model
YAML template to choose which type of deployment the user wants:
- Native deployment: the pipeline deploys the model in a webservice using AML, runs a smoke test, etc. (current behavior).
- Docker image: the pipeline generates an artifact with this packaged model instead of deploying it as a webservice.
After the package has been created, a kubectl
command may connect to the targeted AKS and run the docker image
@mariamedp I would like to help the team on this. I am advising a customer using our current accelerator and their use case is exactly what you are describing now. Just ping through Teams. MS alias: vyle