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 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."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"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 tool makes our ML model development a bit more efficient because everything is in one environment."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The deployment is very good, where you only need to press a few buttons."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"We have found KNIME valuable when it comes to its visualization."
"What I like most about KNIME is that it's user-friendly. It's a low-code, no-code tool, so students don't need coding knowledge. You can make use of different kinds of nodes. KNIME even has a good description of each node."
"It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea."
"Overall KNIME serves its purpose and does a good job."
"This open-source product can compete with category leaders in ELT software."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"It is very fast to develop solutions."
"KNIME is quite scalable, which is one of the most important features that we found."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"SageMaker would be improved with the addition of reporting services."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"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 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."
"The product must provide better documentation."
"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."
"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"It's very general in terms of architecture, and as a result, it doesn't support efficient running. That is, the speed needs to be improved."
"The data visualization part is the area most in need of improvement."
"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."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages."
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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