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."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 ease of use in connecting to our cluster machines."
"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."
"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 most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"We have found KNIME valuable when it comes to its visualization."
"This open-source product can compete with category leaders in ELT software."
"It's a very powerful and simple tool to use."
"Easy to connect with every database: We use queries from SQL, Redshift, Oracle."
"The solution allows for sharing model designs and model operations with other data analysts."
"The most valuable is the ability to seamlessly connect operators without the need for extensive programming."
"It is a stable solution...It is a scalable solution."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"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."
"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."
"It could be easier to use."
"The documentation needs a proper rework. "
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."
"The predefined workflows could use a bit 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."
"When deploying models on a regular system, it works fine. However, when accuracy is a priority, hyperparameter tuning is necessary. Currently, KNIME doesn't have the best tools for this which they could improve in this area."
"I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script."
Earn 20 points
H2O.ai is ranked 19th 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, Weka and Domino Data Science Platform.
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