We performed a comparison between Cloudera Data Science Workbench and H2O.ai based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"The Cloudera Data Science Workbench is customizable and easy to use."
"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."
"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."
"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 tool's MLOps is not good. It's pricing also needs to improve."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"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."
"I would like to see more features related to deployment."
"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."
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Cloudera Data Science Workbench is ranked 17th in Data Science Platforms with 2 reviews while H2O.ai is ranked 19th in Data Science Platforms. Cloudera Data Science Workbench is rated 7.0, while H2O.ai is rated 7.6. The top reviewer of Cloudera Data Science Workbench writes "Useful for data science modeling but improvement is needed in MLOps and pricing ". On the other hand, the top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Microsoft Azure Machine Learning Studio and Dataiku Data Science Studio, whereas H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku Data Science Studio and Microsoft Azure Machine Learning Studio.
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