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 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."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"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 were able to use the product to automate processes."
"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."
"We've had no problems with SageMaker's stability."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data."
"It is a stable solution...It is a scalable solution."
"The product is open-source and therefore free to use."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"The most useful features are the readily available extensions that speed up the work."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"The solution needs to be cheaper since it now charges per document for extraction."
"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 is complex to use."
"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."
"The solution requires a lot of data to train the model."
"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 product must provide better documentation."
"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"When deploying models on a regular system, it works fine. However, when accuracy is a priority, hyperparameter tuning is necessary. Currently, KNIME doesn't have the best tools for this which they could improve in this area."
"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."
"Data visualization needs improvement."
"From the point of view of the interface, they can do a little bit better."
"The documentation needs a proper rework. "
"There should be better documentation and the steps should be easier."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Amazon SageMaker is rated 7.4, 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 Amazon Comprehend, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and SAS Analytics. See our Amazon SageMaker vs. KNIME 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.
