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 solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"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."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"We were able to use the product to automate processes."
"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."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"in terms of the simplicity, I think the SPSS basic can handle it."
"The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS."
"SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools."
"It has the ability to easily change any variable in our research."
"One feature I found very valuable was the analysis of variance (ANOVA)."
"The most valuable feature is its robust statistical analysis capabilities."
"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 feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can into multidimensional setup space. It's the multidimensional space facility that is most useful."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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."
"Lacking in some machine learning pipelines."
"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 pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"The solution requires a lot of data to train the model."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"I'd like to see them use more artificial intelligence. It should be smart enough to do predictions and everything based on what you input."
"The design of the experience can be improved."
"I would like SPSS to improve its integration with other data-filing IBM tools. I also think its duration with data, utilization, and graphics could be better."
"There is a learning curve; it's not very steep, but there is one."
"It could allow adding color to data models to make them easier to interpret."
"It could provide even more in the way of automation as there are many opportunities."
"This solution is not suitable for use with Big Data."
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.
