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."
"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."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"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 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."
"I've tried to utilize KNIME to the fullest extent possible to replace Excel."
"The most useful features are the readily available extensions that speed up the work."
"The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"It's a huge tool with machine learning features as well."
"Easy to connect with every database: We use queries from SQL, Redshift, Oracle."
"Overall KNIME serves its purpose and does a good job."
"The model management features could be improved."
"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."
"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."
"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."
"There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."
"From the point of view of the interface, they can do a little bit better."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
"The ability to handle large amounts of data and performance in processing need to be improved."
"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."
"There should be better documentation and the steps should be easier."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"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."
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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