We performed a comparison between Amazon SageMaker and KNIME 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 solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"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 few projects we have done have been promising."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"We were able to use the product to automate processes."
"We've had no problems with SageMaker's stability."
"The solution is good for teaching, since there is no need to code."
"I've tried to utilize KNIME to the fullest extent possible to replace Excel."
"It's a coding-less opportunity to use AI. This is the major value for me."
"Automation is most valuable. It allows me to automatically download information from different sources, and once I create a workflow, I can apply it anytime I want. So, there is efficiency at the same time."
"This solution is easy to use and it can be used to create any kind of model."
"Since KNIME is a no-code platform, it is easy to work with."
"The most valuable is the ability to seamlessly connect operators without the need for extensive programming."
"We can deploy the solution in a cluster as well."
"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."
"There are other better solutions for large data, such as Databricks."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"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."
"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 solution requires a lot of data to train the model."
"KNIME can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile."
"I've had some problems integrating KNIME with other solutions."
"The predefined workflows could use a bit of improvement."
"From the point of view of the interface, they can do a little bit better."
"KNIME is not scalable."
"The ability to handle large amounts of data and performance in processing need to be improved."
"I would prefer to have more connectivity."
"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."
Amazon SageMaker is ranked 5th in Data Science Platforms with 18 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Amazon SageMaker is rated 7.2, while KNIME is rated 8.2. 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 KNIME writes "A low-code platform that reduces data mining time by linking script". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and H2O.ai, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Weka and Tableau. See our Amazon SageMaker vs. KNIME report.
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