What are the disadvantages of Azure's ML vs a pure code approach (R/SKlearn) Good Day,
Microsoft offers their Azure Machine Learning Platform: https://azure.microsoft.com/en-ca/services/machine-learning/

Azure Machine Learning is designed for applied machine learning. Use best-in-class algorithms and a simple drag-and-drop interface—and go from idea to deployment in a matter of clicks.
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Use Azure Machine Learning to deploy your model into production as a web service in minutes—a web service that can be called from any device, anywhere, and that can use any data source.

By their demo and their photos online it looks like a simple GUI application you can drag and drop your various pre-processing elements, estimators, and testing schemes and appears to make it easy to get started on Machine learning projects.
What no website selling a product will do is advertise their downsides.  With a GUI like Weka you lack the ability to fine tune and tweek the different parameters.
Azure ML seems to allow hooks into Python or R to give you more control and it sure would be appealing to have to do less work to get the same results.

What are the known disadvantages of using Azure ML for ML projects vs writing the same process in code (e.g. Sci-kit Learn or in R)?
I learned the Azure ML platform after scikit-learn (Python) and caret (R).

Advantages:


Low or no coding involved. It is more like Orange in that you set up the ML exercise as a large process flow. It is easier for non-technical people to get started with.
Easy to convert to production-ready. You click a button and it deploys a web-service that you could send to a programmer to use. In Python, you would need to add another library and in R it is even dicier.


Disadvantages:


Fewer algorithms (e.g. no XGBoost) and other transformations (e.g. NLP) built-in. You can of course reference Python and R to do this, but that is much tougher than using the GUI. If you can figure out how to call Python/R from within Azure ML, you likely can do it on your own.
Price.