We performed a comparison between Amazon SageMaker and IBM SPSS Statistics 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."I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"We've had no problems with SageMaker's stability."
"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."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"Allows you to create API endpoints."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."
"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."
"The most valuable features are the solution is easy to use, training new users is not difficult, and our usage is comprehensive because the whole service is beneficial."
"The most valuable feature is the user interface because you don't need to write code."
"The most valuable features are the small learning curve and its ability to hold a lot of data."
"The most valuable feature is its robust statistical analysis capabilities."
"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."
"One feature I found very valuable was the analysis of variance (ANOVA)."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"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 training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"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."
"The product must provide better documentation."
"Lacking in some machine learning pipelines."
"The solution is complex to use."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"I know that SPSS is a statistical tool but it should also include a little bit of analytical behavior. You can call it augmented analysis or predictive analysis. The bottom line is it should have more graphical and analytical capabilities."
"The product should provide more ways to import data and export results that are user-friendly for high-level executives."
"There is a learning curve; it's not very steep, but there is one."
"Technical support needs some improvement, as they do not respond as quickly as we would like."
"I think the visualization and charting should be changed and made easier and more effective."
"SPSS is a tool that's been around since the late 60s, and it's the universal worldwide standard for quantitative social science data analysis. That said, it does seem a bit strange to me that the graphical output functions are so clunky after all these years. The output of charts and graphs that SPSS produces is hideous."
"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."
"Better documentation on how to use macros."
Amazon SageMaker is ranked 5th in Data Science Platforms with 18 reviews while IBM SPSS Statistics is ranked 8th in Data Science Platforms with 36 reviews. Amazon SageMaker is rated 7.2, while IBM SPSS Statistics 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 Statistics writes "Enhancing survey analysis that provides valued insightfulness". 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 Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio, IBM SPSS Modeler and KNIME. See our Amazon SageMaker vs. IBM SPSS Statistics 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.
