We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"The most valuable feature is data normalization."
"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."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"The interface is very intuitive."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"The solution is very good for data mining or any mining issues."
"he solution is scalable."
"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."
"The most valuable feature is the decision tree creation."
"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."
"Good data management and analytics."
"The solution is able to handle quite large amounts of data beautifully."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"The data cleaning functionality is something that could be better and needs to be improved."
"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 be more customizable. There should be more algorithms."
"A problem that I encountered was that I had to pay for the 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."
"Integration with social media would be a valuable enhancement."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"The data preparation capabilities need to be improved."
"The user interface of the solution needs improvement. It needs to be more visual."
"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"Virtualization could be much better."
"The ease of use can be improved. When you are new it seems a bit complex."
"The visualization of the models is not very attractive, so the graphics should be improved."
"Technical support could be improved."
"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."
"When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly."
"From a developer's perspective, I find the price of this solution high."
"The licensing cost is very cheap. It's less than $50 a month."
"There is a license required for this solution."
"I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
"This solution is for large corporations because not everybody can afford it."
"The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."
Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.
Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 17 reviews while SAS Enterprise Miner is ranked 10th in Data Science Platforms with 10 reviews. Microsoft Azure Machine Learning Studio is rated 7.6, while SAS Enterprise Miner is rated 7.6. 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 SAS Enterprise Miner writes "Good GUI, an easy initial setup, and very flexible". Microsoft Azure Machine Learning Studio is most compared with Databricks, Dataiku Data Science Studio, IBM Watson Studio, Alteryx and Amazon SageMaker, whereas SAS Enterprise Miner is most compared with IBM SPSS Modeler, SAS Analytics, RapidMiner, KNIME and Amazon SageMaker. See our Microsoft Azure Machine Learning Studio vs. SAS Enterprise Miner report.
See our list of best Data Science Platforms vendors.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.