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 product aggregates everything we need to build and deploy machine learning models in one place."
"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."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"The deployment is very good, where you only need to press a few buttons."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"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 few projects we have done have been promising."
"I think the features of the actual ability to forecast and pull trends and correlations has been really good."
"The most valuable features are the analytics and reporting."
"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 documentation must be made clearer and more user-friendly."
"SageMaker would be improved with the addition of reporting services."
"The solution requires a lot of data to train the model."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"The solution needs to be cheaper since it now charges per document for extraction."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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 Microsoft Azure Machine Learning Studio, whereas SAP Predictive Analytics is most compared with IBM Watson Studio, Microsoft Azure Machine Learning Studio, IBM SPSS Modeler, Domino Data Science Platform and Alteryx. See our Amazon SageMaker vs. SAP Predictive Analytics report.
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