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."We've had no problems with SageMaker's stability."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The deployment is very good, where you only need to press a few buttons."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"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."
"They are doing a good job of evolving."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The most valuable features mainly include factor analysis, correlation analysis, and geographic analysis."
"One feature I found very valuable was the analysis of variance (ANOVA)."
"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."
"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."
"SPSS is quite robust and quicker in terms of providing you the output."
"The most valuable feature is the user interface because you don't need to write code."
"IBM SPSS Statistics depends on AI."
"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."
"SageMaker would be improved with the addition of reporting services."
"The product must provide better documentation."
"The solution is complex to use."
"The documentation must be made clearer and more user-friendly."
"AI is a new area and AWS needs to have an internship training program available."
"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."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"This solution is not suitable for use with Big Data."
"Needs more statistical modelling functions."
"Perhaps in terms of visualization. It's not really easy to do some data visualization, just simple, descriptive analysis in SPSS. I think that could be an area for improvement."
"It could allow adding color to data models to make them easier to interpret."
"IBM SPSS Statistics could improve the visual outputs where you are producing, for example, a graph for a company board of directors, or an advert."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"Better documentation on how to use macros."
"The technical support should be improved."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while IBM SPSS Statistics is ranked 7th 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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