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."
"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."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"It has allowed us to easily implement advanced analytics into various processes."
"I've never had any problems with stability."
"We have been able to appreciate the considerable reduction in prototyping time."
"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."
"Easy to use, stable, and powerful."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
"The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine."
"I would rate the stability of KNIME a ten out of ten."
"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."
"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."
"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."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"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 program is not fit for handling very large files or databases (greater than 1GB); it gets too slow and has a tendency to crash easily."
"KNIME is not good at visualization."
"The predefined workflows could use a bit of improvement."
"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."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
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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