We performed a comparison between IBM SPSS Modeler and IBM Watson 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."We use analytics with the visual modeling capability to leverage productivity improvements."
"Very good data aggregation."
"It continues to be a very flexible platform, so that it handles R and Python and other types of technology. It seems to be growing with additional open-source movement out there on different platforms."
"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."
"It helped me in that I didn't need to write them by hand, and I could get a result in one or two minutes. That helped me a lot."
"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’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it"
"New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data."
"IBM Watson Studio consistently automates across channels."
"Watson Studio is very stable."
"The scalability of IBM Watson Studio is great."
"The solution is very easy to use."
"It has greatly improved the performance because it is standardized across the company."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."
"It is a very stable and reliable solution."
"Expensive to deploy solutions. You need to buy an extra deployment unit."
"Initial setup of the software was complex, because of our own problems within the government."
"If IBM could add some of the popular models into the SPSS for further analysis, like popular regression models, I think that would be a helpful improvement."
"Neural networks are quite simple, and now neural networks are evolving to these architecture related to deep learning, etc. They didn't incorporate this in IBM SPSS Modeler."
"The standard package (personal) is not supported for database connection."
"When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems."
"It would be beneficial if the tool would include more well-known machine learning algorithms."
"I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"The solution's interface is very slow at times."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"I want IBM's technical support team to provide more specific answers to queries."
"The main challenge lies in visibility and ease of use."
"The initial setup was complex."
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
"Some of the solutions are really good solutions but they can be a little too costly for many."
IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews while IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews. IBM SPSS Modeler is rated 8.0, while IBM Watson Studio is rated 8.2. 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 IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". IBM SPSS Modeler is most compared with Microsoft Power BI, KNIME, IBM SPSS Statistics, RapidMiner and Dataiku, whereas IBM Watson Studio is most compared with Databricks, Azure OpenAI, Microsoft Azure Machine Learning Studio, Google Vertex AI and Cloudera Data Science Workbench. See our IBM SPSS Modeler vs. IBM Watson Studio report.
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