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 makes our ML model development a bit more efficient because everything is in one environment."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"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 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."
"We were able to use the product to automate processes."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The most valuable is the ability to seamlessly connect operators without the need for extensive programming."
"Overall KNIME serves its purpose and does a good job."
"All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"The most useful features are the readily available extensions that speed up the work."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."
"The solution requires a lot of data to train the model."
"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."
"There are other better solutions for large data, such as Databricks."
"The solution is complex to use."
"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 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."
"Lacking in some machine learning pipelines."
"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"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 would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script."
"It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end."
"The overall user experience feels unpolished. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
"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 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.
