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 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."
"We were able to use the product to automate processes."
"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."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"We've had no problems with SageMaker's stability."
"The deployment is very good, where you only need to press a few buttons."
"Allows you to create API endpoints."
"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."
"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."
"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."
"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."
"in terms of the simplicity, I think the SPSS basic can handle it."
"It is a modeling tool with helpful automation."
"It offers very good visualization."
"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."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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."
"The solution is complex to use."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"Lacking in some machine learning pipelines."
"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 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."
"Better documentation on how to use macros."
"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."
"Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"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."
"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."
"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."
"One of the areas that should be similar to Minitabs is the use of blogs. The Minitabs blog helps users understand the tools and gives lots of practical examples. Following the SPSS manual is cumbersome. It's a good, exhaustive manual, but it's not practical to use. With Minitabs, you can go to the blogs and find specific articles written about various components and it's very helpful. Without blogs, we find SPSS more complicated."
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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