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
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.