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 superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The deployment is very good, where you only need to press a few buttons."
"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."
"We've had no problems with SageMaker's stability."
"Allows you to create API endpoints."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"We were able to use the product to automate processes."
"It is just a lot faster. So you do not have to write a bunch of code, you can throw that stuff on there pretty quickly and do prototyping quickly."
"Our business units' capabilities with SPSS Modeler is high. They no longer waste time on modeling and algorithms, meaning they are not coding any more. For example, segmentation projects now take one to three months, rather than six months to a year, as before."
"Stability is good."
"Automated modelling, classification, or clustering are very useful."
"The quality is very good."
"We use analytics with the visual modeling capability to leverage productivity improvements."
"Very good data aggregation."
"Our go live process has been slightly enhanced compared to the previous programmatic process. There is now a faster time to production from the business end."
"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."
"The solution is complex to use."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"There are other better solutions for large data, such as Databricks."
"The solution requires a lot of data to train the model."
"AI is a new area and AWS needs to have an internship training program available."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"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."
"The forecasting could be a bit easier."
"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 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."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"Initial setup of the software was complex, because of our own problems within the government."
"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."
"Requires more development."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews. Amazon SageMaker is rated 7.4, 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, IBM SPSS Statistics, RapidMiner and SAS Enterprise Miner. See our Amazon SageMaker vs. IBM SPSS Modeler report.
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