Anonymous UserContracts Manager at a program development consultancy
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"Very good data aggregation."
"It is a great product for running statistical analysis."
"Automation is great and this product is very organized."
"You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after."
"The supervised models are valuable. It is also very organized and easy to use."
"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."
"Requires more development."
"It would be good if IBM added help resources to the interface."
"Dimension reduction should be classified separately."
"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."
"Time Series or forecasting needs to be easier. It is a very important feature, and it should be made easier and more automated to use. For instance, for logistic regression, binary or multinomial is used automatically based on the type of the target variable. I wish they can make Time Series easier to use in a similar way."
"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."
"This tool, being an IBM product, is pretty expensive."
"Its price is okay for a company, but for personal use, it is considered somewhat expensive."
"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."
IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.
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.
IBM SPSS Modeler is ranked 8th in Data Science Platforms with 5 reviews while Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 14 reviews. IBM SPSS Modeler is rated 8.4, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of IBM SPSS Modeler writes "User-friendly, and it gives you a lot of visibility through features like comparing fiscal quarters". 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 IBM SPSS Statistics, Alteryx, KNIME, IBM Watson Studio and Microsoft BI, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, IBM Watson Studio, Alteryx, Dataiku Data Science Studio and Amazon Comprehend. See our IBM SPSS Modeler vs. Microsoft Azure Machine Learning Studio report.
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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.