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."The few projects we have done have been promising."
"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"They are doing a good job of evolving."
"Allows you to create API endpoints."
"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."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"in terms of the simplicity, I think the SPSS basic can handle it."
"You can quickly build models because it does the work for you."
"It has the ability to easily change any variable in our research."
"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."
"The software offers consistency across multiple research projects helping us with predictive analytics capabilities."
"The most valuable feature is its robust statistical analysis capabilities."
"One feature I found very valuable was the analysis of variance (ANOVA)."
"Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"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."
"SageMaker would be improved with the addition of reporting services."
"Lacking in some machine learning pipelines."
"The documentation must be made clearer and more user-friendly."
"There are other better solutions for large data, such as Databricks."
"The product must provide better documentation."
"The product should provide more ways to import data and export results that are user-friendly for high-level executives."
"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."
"The statistics should be more self-explanatory with detailed automated reports."
"Better documentation on how to use macros."
"There is a learning curve; it's not very steep, but there is one."
"The technical support should be improved."
"Needs more statistical modelling functions."
"The solution needs to improve forecasting using time series analysis."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while IBM SPSS Statistics is ranked 8th in Data Science Platforms with 36 reviews. Amazon SageMaker is rated 7.4, 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 Dataiku, whereas IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio, IBM SPSS Modeler and Anaconda. 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.