This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Tech Community.

Today, we are announcing the public preview of the ability to use custom Docker containers in Azure Machine Learning online endpoints. In combination with our new 2.0 CLI, this feature enables you to deploy a custom Docker container while getting Azure Machine Learning online endpoints’ built-in monitoring, scaling, and alerting capabilities.

Below, we walk you through how to use this feature to deploy TensorFlow Serving with Azure Machine Learning. The full code is available in our samples repository.

Sample deployment with TensorFlow Serving

To deploy a TensorFlow model with TensorFlow Serving, first create a YAML file:

name: tfserving-endpoint type: online auth_mode: aml_token traffic: tfserving: 100 deployments: - name: tfserving model: name: tfserving-mounted version: 1 local_path: ./half_plus_two environment_variables: MODEL_BASE_PATH: /var/azureml-app/azureml-models/tfserving-mounted/1 MODEL_NAME: half_plus_two environment: name: tfserving version: 1 docker: image: docker.io/tensorflow/serving:latest inference_config: liveness_route: port: 8501 path: /v1/models/half_plus_two readiness_route: port: 8501 path: /v1/models/half_plus_two scoring_route: port: 8501 path: /v1/models/half_plus_two:predict instance_type: Standard_F2s_v2 scale_settings: scale_type: manual instance_count: 1 min_instances: 1 max_instances: 2

Then create your endpoint:

az ml endpoint create -f endpoint.yml

And that’s it! You now have a scalable TensorFlow Serving endpoint running on Azure ML-managed compute.

Next steps

Read our documentation
See the sample with TorchServe
Learn more about our Azure-built inference images.
Look out for future samples showing ML.NET and R support

Custom containers in Azure Machine Learning managed online endpoints

Sample deployment with TensorFlow Serving

Next steps

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