We performed a comparison between Amazon SageMaker and IBM SPSS Modeler 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."I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"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 most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"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."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The few projects we have done have been promising."
"We have full control of the data handling process."
"IBM was chosen because of usability. It's point and click, whereas the other out-of-the box-solution, or open-source solutions, require full-on programming and a much higher skill level."
"It continues to be a very flexible platform, so that it handles R and Python and other types of technology. It seems to be growing with additional open-source movement out there on different platforms."
"It gives you a GUI interface, which is a lot more user-friendly and easier to use compared to writing R scripts or Python."
"We are using it either for workforce deployment or to improve our operations."
"I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions."
"Very good data aggregation."
"It handles large data better than the previous system that we were using, which was basically Excel and Access. We serve upwards of 300,000 parts over a 150 regions and we need to crunch a lot of numbers."
"SageMaker would be improved with the addition of reporting services."
"AI is a new area and AWS needs to have an internship training program available."
"There are other better solutions for large data, such as Databricks."
"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."
"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."
"The solution is complex to use."
"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 challenge for the very technical data scientists: It is constraining for them."
"When you are not using the product, such as during the pandemic where we had worldwide lockdowns, you still have to pay for the licensing."
"I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions."
"We have run into a few problems doing some entity matching/analytics."
"I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities."
"The integration with sources and visualisation needs some improvement. The scalability needs improvement."
"The standard package (personal) is not supported for database connection."
"Initial setup of the software was complex, because of our own problems within the government."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews. Amazon SageMaker is rated 7.4, while IBM SPSS Modeler 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 Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Dataiku, whereas IBM SPSS Modeler is most compared with Microsoft Power BI, KNIME, IBM SPSS Statistics, RapidMiner and SAS Enterprise Miner. See our Amazon SageMaker vs. IBM SPSS Modeler report.
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