Microsoft have recently released an updated version of their Azure Machine learning service. At Elastacloud we have been using AML since the first release to deploy machine learning models to the cloud. AML provides a platform to develop, train, test, deploy, manage, and track machine learning models but it is mostly the deploy and manage part that Elastacloud have made use of, so my article is going to focus on these aspects.
Based on feedback from the community Microsoft have made sweeping changes to the service which essentially mean it is a new product. Some of the major changes that users will have to adjust to are: -
Don’t need individual experimentation and modelmanagement accounts, just a single workspace
New Python SDKs
New Azure Machine Learning CLI extension
My view on the removal of the workbench is neutral, I never used it previously other than to launch the CLI. My understanding is that it was an underused tool, with most people preferring to develop their models in an IDE such as VS Code or even in Jupyter Notebooks.
Possible use cases for the Microsoft Azure ML service
There are two new Python SDKs; the Machine learning SDK and the Data prep SDK. The ML SDK, in Microsoft’s words, “is used by data scientists and AI developers to build and run machine learning workflows upon the Azure Machine Learning service”.
In a recent Elastacloud project we have deployed a number of predictive machine learning models, as a web service, for a customer using the new AML service. I used the Machine learning SDK to complete this task and after just a few teething problems I found that the SDK was easy to work with and certainly felt like a tool that made me more productive.
One of the requirements for this web service required some consideration on how to best create the service; different models should be loaded and used to generate the predictions based on the day of the week. This requirement arises because our customer always needs forecasts for the next two days and the next two business days, meaning that on a Friday, for example, they want forecasts for the next two days (Saturday and Sunday) and the next two business days (Monday and Tuesday) whereas on a Monday they only need them for the next two business days (Tuesday and Wednesday).
Therefore, we have three different models (one day ahead, two days ahead, weekend) deployed to the same service, with multiple dependencies (e.g. *.py files). Azure ML made the creation of the service very easy, as demonstrated in the code examples below.
Creating a Docker image
Models are deployed in Docker images to Azure Kubernetes Service (AKS) or Azure Container Instances (ACI). The code excerpt below shows how simple it is to create this image in only two lines; image_config contains the required score.py file alongside the optional dependencies and a conda .env file. The image is then built with ContainerImage.create where the already registered models are provided.
Creating the docker image
Deploying the service
Once we have a successfully built Docker image we can deploy it to AKS or ACI as a web service. This, again, is very easy to do with the SDK as shown in the image below. We only need to define a configuration, with AksWebservice.deploy_configuration() (gives default configuration), then use the deploy_from_image method, providing our Docker image as one of the arguments.
Creating the AKS web service
And so we have successfully deployed our machine learning models as a web service! Now we can get the scoring URI (aks_service.scoring_uri) and the access keys (aks_service.get_keys) and start making requests to our machine learning models.