We performed a comparison between Amazon SageMaker and IBM SPSS Modeler 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 most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"Allows you to create API endpoints."
"The few projects we have done have been promising."
"We've had no problems with SageMaker's stability."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The deployment is very good, where you only need to press a few buttons."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"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."
"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."
"I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions."
"We have been able to do some predictive modeling with it"
"Very good data aggregation."
"Stability is good."
"We are using it either for workforce deployment or to improve our operations."
"It is very scalable for non-technical people."
"There are other better solutions for large data, such as Databricks."
"The documentation must be made clearer and more user-friendly."
"Lacking in some machine learning pipelines."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"The solution needs to be cheaper since it now charges per document for extraction."
"The solution is complex to use."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"The solution requires a lot of data to train the model."
"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."
"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."
"Expensive to deploy solutions. You need to buy an extra deployment unit."
"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."
"The platform that you can deploy it on needs improvement because I think it is Windows only. I do not think it can run off a Red Hat, like the server products. I am pretty sure it is Windows and AIX only."
"I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities."
"Customer support is hard to contact."
"The platform's cloud version needs improvements."
Amazon SageMaker is ranked 5th in Data Science Platforms with 18 reviews while IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews. Amazon SageMaker is rated 7.2, while IBM SPSS Modeler is rated 8.0. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". On the other hand, the top reviewer of IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Microsoft Azure Machine Learning Studio, whereas IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and SAS Enterprise Miner. See our Amazon SageMaker vs. IBM SPSS Modeler report.
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