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 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."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The few projects we have done have been promising."
"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."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."
"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."
"You can quickly build models because it does the work for you."
"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."
"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 most valuable features mainly include factor analysis, correlation analysis, and geographic analysis."
"Most of the product features are good but I particularly like the linear regression analysis."
"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."
"The solution is complex to use."
"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 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 documentation must be made clearer and more user-friendly."
"The solution requires a lot of data to train the model."
"SageMaker would be improved with the addition of reporting services."
"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."
"The solution needs to improve forecasting using time series analysis."
"SPSS slows down the computer or the laptop if the data is huge; then you need a faster computer."
"The solution needs more planning tools and capabilities."
"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."
"There is a learning curve; it's not very steep, but there is one."
"Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them."
"The statistics should be more self-explanatory with detailed automated reports."
"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."
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.
See our list of best Data Science Platforms vendors.
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.