We performed a comparison between Amazon SageMaker and RapidMiner 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."They are doing a good job of evolving."
"We were able to use the product to automate processes."
"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."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"Allows you to create API endpoints."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The data science, collaboration, and IDN are very, very strong."
"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."
"The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically."
"RapidMiner is very easy to use."
"The solution is stable."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"We value the collaboration and governance features because it's a comprehensive platform that covers everything from data extraction to modeling operations in the ML language. RapidMiner is competitive in the ML space."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"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."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"The product must provide better documentation."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"SageMaker would be improved with the addition of reporting services."
"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."
"RapidMiner isn't cheap. It's a complete solution, but it's costly."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"The price of this solution should be improved."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade."
"The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator."
"If they could include video tutorials, people would find that quite helpful."
"Improve the online data services."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while RapidMiner is ranked 6th in Data Science Platforms with 20 reviews. Amazon SageMaker is rated 7.4, while RapidMiner 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 RapidMiner writes "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Microsoft Azure Machine Learning Studio, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku, Tableau and H2O.ai. See our Amazon SageMaker vs. RapidMiner report.
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