We performed a comparison between H2O.ai and KNIME based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"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."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The ease of use in connecting to our cluster machines."
"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."
"We have found KNIME valuable when it comes to its visualization."
"One of the greatest advantages of KNIME is that it can be used by those without any coding experience. those with no coding background can use it."
"It is a stable solution...It is a scalable solution."
"It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured."
"KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data."
"Since KNIME is a no-code platform, it is easy to work with."
"We can deploy the solution in a cluster as well."
"I've never had any problems with stability."
"The model management features could be improved."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"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."
"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."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"KNIME's licensing and data management aren't as straightforward relative to Alteryx. Alteryx's tools are more sophisticated, so you need fewer to use it compared to KNIME. I think tab implementation could be easier, too."
"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."
"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."
"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."
"One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
"Data visualization needs improvement."
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 Data Science Studio, Microsoft Azure Machine Learning Studio and IBM Watson Studio, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku Data Science Studio and Domino Data Science Platform.
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