We performed a comparison between Microsoft Azure Machine Learning Studio and SAS Enterprise Miner 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 learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"The solution is really scalable."
"It's easy to deploy."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"It has helped in reducing the time involved for coding using R and/or Python."
"It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"The most valuable feature is the decision tree creation."
"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."
"I like the way the product visually shows the data pipeline."
"The solution is able to handle quite large amounts of data beautifully."
"Good data management and analytics."
"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."
"The technical support is very good."
"he solution is scalable."
"The solution's initial setup process is complicated."
"The solution should be more customizable. There should be more algorithms."
"The price of the solution has room for improvement."
"The interface is a bit overloaded."
"It would be nice if the product offered more accessibility in general."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"The initial setup time of the containers to run the experiment is a bit long."
"There should be data access security, a role level security. Right now, they don't offer this."
"Technical support could be improved."
"The user interface of the solution needs improvement. It needs to be more visual."
"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 product must provide better integration with cloud-native technologies."
"The initial setup is challenging if doing it for the first time."
"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."
"The visualization of the models is not very attractive, so the graphics should be improved."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 50 reviews while SAS Enterprise Miner is ranked 16th in Data Science Platforms with 13 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 "A stable product that is easy to deploy and can be used for structured and unstructured data mining". Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Google Cloud AI Platform, whereas SAS Enterprise Miner is most compared with SAS Visual Analytics, IBM SPSS Modeler, RapidMiner, SAS Analytics and KNIME. 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.