We performed a comparison between H2O.ai 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."Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."
"The ease of use in connecting to our cluster machines."
"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"It's a very powerful and simple tool to use."
"Easy to use, stable, and powerful."
"Easy to connect with every database: We use queries from SQL, Redshift, Oracle."
"We can deploy the solution in a cluster as well."
"This solution is easy to use and especially good at data preparation and wrapping."
"I would rate the stability of KNIME a ten out of ten."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"From a user-friendliness perspective, it's a great tool."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"I would like to see more features related to deployment."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"The model management features could be improved."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."
"They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning."
"I've had some problems integrating KNIME with other solutions."
"There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"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."
"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."
"The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon."
Earn 20 points
H2O.ai is ranked 20th in Data Science Platforms while KNIME is ranked 4th in Data Science Platforms with 50 reviews. H2O.ai is rated 7.6, while KNIME is rated 8.2. The top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku, Microsoft Azure Machine Learning Studio and IBM Watson Studio, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Pentaho Business Analytics. See our H2O.ai vs. KNIME report.
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