![]() ![]() My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners." "As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. Additionally, the setup process for the solution could be made easier." "Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. The components that we're using might have something to do with this." "This solution could be improved if they could integrate the data pipeline scheduling part for their interface." "The price of the solution has room for improvement." "In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data." "Overall, the icons in the solution could be improved to provide better guidance to users. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It takes some time to get exposed to all the concepts. More Microsoft Azure Machine Learning Studio Pros → All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning." ![]() ![]() With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. Though there's a bit of work during data cleansing, that's normal and can't be avoided. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. It is easy to use and to connect for analytics." "The initial setup is very simple and straightforward." "Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful." "Azure's AutoML feature is probably better than the competition." "Their support is helpful." "In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It's simple to connect and view the results. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon." "The most valuable feature of Microsoft Azure Machine Learning Studio is the ease of use for starting projects. ![]() "The solution is really scalable." "Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. ![]()
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