Disclaimer: this isn't about the old classic drag and drop ML studio!
Developing machine learning models or data science applications on Microsoft stack is not a very nice experience. The main tool is Azure Machine Learning Studio and it doesn't provide a good story for DevOps, Source Control or using open source ML tools/IDEs heavily used by data scientists.
A new cloud service has been recently added to Azure to solve those problems and make ML and app development go hand in hand and in harmony.
Meet Azure Machine Learning Service! It's a one-stop shop that you can use to develop, deploy and manage machine learning models.
In this demo-packed session, we will cover the main features of this service. We will start with basic topics by developing a simple model and deploying it as web service in a CI/CD fashion. Then we will move to more advanced use cases like automatic model selection, consuming ONNX models and distributed model training.
P.S. If you wonder if this is the ML workbench tool? Not exactly as the workbench has been deprecated and replaced with this cloud service along with its SDK & APIs.