We performed a comparison between Amazon SageMaker and SAP Predictive Analytics 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 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'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 solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"They are doing a good job of evolving."
"We've had no problems with SageMaker's stability."
"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 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 most valuable features are the analytics and reporting."
"I think the features of the actual ability to forecast and pull trends and correlations has been really good."
"The documentation must be made clearer and more user-friendly."
"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."
"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 solution needs to be cheaper since it now charges per document for extraction."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"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 requires a lot of data to train the model."
"This solution works for acquired data but not live, real-time data."
Earn 20 points
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while SAP Predictive Analytics is ranked 24th in Data Science Platforms. Amazon SageMaker is rated 7.4, while SAP Predictive Analytics is rated 8.6. 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 SAP Predictive Analytics writes "Easy to implement, good data forecasting and reporting". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Dataiku, whereas SAP Predictive Analytics is most compared with IBM SPSS Modeler, IBM Watson Studio, Domino Data Science Platform, Microsoft Azure Machine Learning Studio and Alteryx. See our Amazon SageMaker vs. SAP Predictive Analytics 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.
