Please share with the community what you think needs improvement with Microsoft Azure Machine Learning Studio.
What are its weaknesses? What would you like to see changed in a future version?
I used Azure Machine Learning in a free trial and I had a complete preview of the service. A problem that I encountered was that I had a model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer. I didn't find any option to upload my model, so that I can create my own block and use it in Azure Machine Learning designer. I believe this is a problem because sometimes you have your model created on some other device and you just have a file that you think can be uploaded to Azure Machine Learning and can be tested through a simple drag and drop tool.
The solution should be more customizable. There should be more algorithms. The solution needs more functionality.
On the customer side, the solution should do more to push companion marketing. When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers. The solution should simplify switching between platforms in the studio.
The data cleaning functionality is something that could be better and needs to be improved. There should be special pricing for developers so that they can learn this solution without paying full price.
Some of the terminologies, or the way that the questions are asked, could be stronger. When people use local colloquialisms, it would be better if it understood rather than forwarding it to an agent. If the frontline efficiencies were improved then we could pass this on to our clients. Integration with social media would be a valuable enhancement.
If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice. One of the problems that we had was that you could only execute the model inside the machine learning environment. Comparing this to Databricks, if you create a pipeline, it could be in a notebook and you have all the code and then you can export your notebook to some other tool directly, for example in Jupyter and Spark. If you change tools then you won't lose your assets. I would like to see improvements to make this solution more user-friendly. They need to have some tools, like Apache Airflow, for helping to build workflows. Better tools are needed to bring the data from existing storage into the environment where they can play with it and start to analyze what they already have, on-site. This is what the majority of people would like to do. A feature that would be useful is to have some standard data transportation functions. They have ADF, Azure Data Factory, but it's a little bit heavy to manipulate. If they could have something more user-friendly, like Apache Airflow, it would be very nice.
Operability with R could be improved.