We performed a comparison between Microsoft Azure Machine Learning Studio and RapidMiner based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The solution is very easy to use, so far as our data scientists are concerned."
"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
"Visualisation, and the possibility of sharing functions are key features."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"Auto email and studio are great features."
"The interface is very intuitive."
"ML Studio is very easy to maintain."
"The solution's most beneficial feature is its integration with Azure."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"The data science, collaboration, and IDN are very, very strong."
"RapidMiner for Windows is an excellent graphical tool for data science."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"Microsoft should also include more examples and tutorials for using this product."
"We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. 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. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."
"Integration with social media would be a valuable enhancement."
"The interface is a bit overloaded."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"It would be nice if the product offered more accessibility in general."
"I would appreciate improvements in automation and customization options to further streamline processes."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"I would like to see more integration capabilities."
"The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade."
"I think that they should make deep learning models easier."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator."
"Improve the online data services."
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Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 50 reviews while RapidMiner is ranked 6th in Data Science Platforms with 19 reviews. Microsoft Azure Machine Learning Studio is rated 7.6, while RapidMiner is rated 8.6. The top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". On the other hand, the top reviewer of RapidMiner writes "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Amazon Augmented AI, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and IBM SPSS Modeler. See our Microsoft Azure Machine Learning Studio vs. RapidMiner report.
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