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."Allows you to create API endpoints."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"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 feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"The deployment is very good, where you only need to press a few buttons."
"The few projects we have done have been promising."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"The data science, collaboration, and IDN are very, very strong."
"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 documentation for this solution is very good, where each operator is explained with how to use it."
"The solution is stable."
"It's helpful if you want to make informed decisions using data. We can take the information, tease out the attributes, and label everything. It's suitable for profiling and forecasting in any industry."
"RapidMiner is a no-code machine learning tool. I can install it on my local machine and work with smaller datasets. It can also connect to databases, allowing me to build models directly on the data stored there. RapidMiner offers a wider range of operators than other tools like Dataiku, making it a better option for my needs."
"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 product must provide better documentation."
"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."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"The solution requires a lot of data to train the model."
"The solution is complex to use."
"SageMaker would be improved with the addition of reporting services."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"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."
"One challenge I encountered while implementing RapidMiner was the lack of documentation. Since there aren't as many users, finding resources to learn the tool was initially difficult. To overcome this hurdle, I believe RapidMiner could improve by providing more tutorials tailored for new users."
"I would like to see more integration capabilities."
"I think that they should make deep learning models easier."
"In the Mexican or Latin American market, it's kind of pricey."
"It would be helpful to have some tutorials on communicating with Python."
"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."
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 "A no-code tool that helps to build machine learning models ". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Dataiku, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku, Tableau and H2O.ai. See our Amazon SageMaker vs. RapidMiner report.
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