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 drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"The UI is very user-friendly and that AI is easy to use."
"The solution is very fast and simple for a data science solution."
"The product's standout feature is a robust multi-file network with limited availability."
"Auto email and studio are great features."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."
"We value the collaboration and governance features because it's a comprehensive platform that covers everything from data extraction to modeling operations in the ML language. RapidMiner is competitive in the ML space."
"The solution is stable."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"The data science, collaboration, and IDN are very, very strong."
"The most valuable features are the Binary classification and Auto Model."
"RapidMiner is very easy to use."
"What I like about RapidMiner is its all-in-one nature, which allows me to prepare, extract, transform, and load data within the same tool."
"The price could be improved."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy."
"The product must improve its documentation."
"The solution's initial setup process is complicated."
"The initial setup time of the containers to run the experiment is a bit long."
"The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
"As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased."
"In the Mexican or Latin American market, it's kind of pricey."
"One challenge I encountered while implementing RapidMiner was the lack of documentation. Since there aren't as many users, finding resources to learn the tool was initially difficult. To overcome this hurdle, I believe RapidMiner could improve by providing more tutorials tailored for new users."
"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."
"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."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"I would like to see more integration capabilities."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"It would be helpful to have some tutorials on communicating with Python."
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Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 53 reviews while RapidMiner is ranked 6th in Data Science Platforms with 20 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 "A no-code tool that helps to build machine learning models ". Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Anaconda, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku, Tableau and IBM SPSS Modeler. See our Microsoft Azure Machine Learning Studio vs. RapidMiner report.
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