The Azure ML service is a service to train and deliver a model as a containerized application. When we have built the model, we can easily deploy it in a container such as Docker, so it is very simple to deploy to the Azure Cloud. The Azure ML service can work in collaboration with Azure Batch AI, advanced hyperparameter tuning services, and Azure Container Instances.
The Azure ML service is different from the Azure ML Studio. The Azure ML Studio is a collaborative visual workspace where we can build, test, and deploy analytics without needing to write code. Models created in the Azure ML Studio cannot be deployed or managed by the Azure ML service.
The basic steps to develop our analytical model with the Azure ML service are as follows:
- Preparing the data
- Developing the model with a rich tool, such as Jupyter Notebook, Visual Studio Code,...
