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."Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"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."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"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."
"They are doing a good job of evolving."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The most valuable features are the small learning curve and its ability to hold a lot of data."
"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."
"One feature I found very valuable was the analysis of variance (ANOVA)."
"The most valuable feature is the user interface because you don't need to write code."
"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."
"The solution has numerous valuable features. We particularly like custom tabs. It's very useful. We end up analyzing a lot of software data, so features related to custom tabs are really helpful."
"Most of the product features are good but I particularly like the linear regression analysis."
"SPSS is quite robust and quicker in terms of providing you the output."
"The solution needs to be cheaper since it now charges per document for extraction."
"Lacking in some machine learning pipelines."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"AI is a new area and AWS needs to have an internship training program available."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"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 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."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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."
"There is a learning curve; it's not very steep, but there is one."
"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."
"Technical support needs some improvement, as they do not respond as quickly as we would like."
"Needs more statistical modelling functions."
"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 reports could be better."
"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."
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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