"The supervised models are valuable. It is also very organized and easy to use."
"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."
"It is a great product for running statistical analysis."
"Automation is great and this product is very organized."
"Very good data aggregation."
"The interface is very intuitive."
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
"It's a great option if you are fairly new and don't want to write too much code."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"Azure's AutoML feature is probably better than the competition."
"The solution is very easy to use, so far as our data scientists are concerned."
"The UI is very user-friendly and that AI is easy to use."
"Requires more development."
"It would be good if IBM added help resources to the interface."
"Dimension reduction should be classified separately."
"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."
"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."
"They should have a desktop version to work on the platform."
"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."
"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 data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
"The interface is a bit overloaded."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"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."
"It would be nice if the product offered more accessibility in general."
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.
Microsoft Azure Machine Learning Will Help You:
With Microsoft Azure Machine Learning You Can:
Microsoft Azure Machine Learning Features:
Microsoft Azure Machine Learning Benefits:
Reviews from Real Users:
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.” - Channing S.l, Owner at Channing Stowell Associates
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.” - Chris P., Tech Lead at a tech services company
"The UI is very user-friendly and the AI is easy to use.” - Mikayil B., CRM Consultant at a computer software company
"The solution is very fast and simple for a data science solution.” - Omar A., Big Data & Cloud Manager at a tech services company
IBM SPSS Modeler is ranked 7th in Data Science Platforms with 5 reviews while Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 16 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 "Has the ability to do templating and transfer it so that we can do multiple types of models and data mining". IBM SPSS Modeler is most compared with IBM Watson Studio, KNIME, IBM SPSS Statistics, Alteryx and RapidMiner, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Dataiku Data Science Studio, IBM Watson Studio, KNIME and Amazon Comprehend. See our IBM SPSS Modeler vs. Microsoft Azure Machine Learning Studio report.
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