dstoolkit-mlops-base
dstoolkit-mlops-base copied to clipboard
microsoft
Support ML teams to accelerate their model deployment to production leveraging Azure
[MLflow](https://mlflow.org/) is becoming the most common model management library. Enabling it by default in the template is a requirement as the new AML SDK version will rely on it more...
Currently, [score.py](https://github.com/microsoft/dstoolkit-mlops-base/blob/main/src/score.py) is the only "src" file that has no main method and thus cannot be easily run locally. It would be nice to to have that to ease testing...
Hi, I've used this for demoing to my customer and I think it would great to show how the azure-pipelines can be used to deploy to higher environments using the...
When training multiple models, the ado pipelines should be able to deploy all trained models into other environments. The change need to be applied to: - Training and registering script:...
Currently the template reruns the scripts in different environments. Although it ensures that automate retraining process works, this functionality should be defined as an integration test on a sample set...
The template currently uses Microsoft-hosted agents to run pipelines in Azure DevOps, which is the simplest way to run the jobs and very useful to set up a quick MLOps...
We want to extend the devops pipeline to integrate github actions and infrastructure-as-code with terraform scripts. The resulting devops repo may follow this structure: devops-pipelines - .ado - pipeline-0 IaC...
AML provides a model profiling functionality which enables team to assess their deployment services (memory consumption, latency, etc): https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-profile-model?pivots=py-sdk During model deployment to AKS (TEST/PROD), it may be useful to...
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...