We performed a comparison between IBM SPSS Modeler and Microsoft Azure Machine Learning Studio 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."It is very scalable for non-technical people."
"We are using it either for workforce deployment or to improve our operations."
"So far, the stability has been rock solid."
"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."
"It will scale up to anything we need."
"Some basic form of feature engineering for classification models. This really quickens the model development process."
"The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that."
"It works fine. I have not had any stability issues; it is always up."
"The most valuable feature is its compatibility with Tensorflow."
"The solution is very easy to use, so far as our data scientists are concerned."
"Visualisation, and the possibility of sharing functions are key features."
"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."
"The most valuable feature of Microsoft Azure Machine Learning Studio is the ease of use for starting projects. It's simple to connect and view the results. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It is easy to use and to connect for analytics."
"I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results."
"Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful."
"The solution is scalable."
"When you are not using the product, such as during the pandemic where we had worldwide lockdowns, you still have to pay for the licensing."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"It would be beneficial if the tool would include more well-known machine learning algorithms."
"I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it."
"The platform's cloud version needs improvements."
"C&DS will not meet our scalability needs."
"I can say the solution is outdated."
"It is not integrated with Qlik, Tableau, and Power BI."
"The platform's integration feature could be better."
"The speed of deployment should be faster, as should testing."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"It could use to add some more features in data transformation, time series and the text analytics section."
"In terms of improvement, I'd like to have more ability to construct and understand the detailed impact of the variables on the model. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" it wasn't great."
"The regulatory requirements of the product need improvement."
"They should have a desktop version to work on the platform."
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IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 51 reviews. IBM SPSS Modeler is rated 8.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and Weka, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our IBM SPSS Modeler vs. Microsoft Azure Machine Learning Studio report.
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