We performed a comparison between H2O.ai and IBM Watson Studio 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."The ease of use in connecting to our cluster machines."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"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."
"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."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"Watson Studio is very stable."
"The solution is very easy to use."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"The system's ability to take a look at data, segment it and then use that data very differently."
"Stability-wise, it is a great tool."
"The scalability of IBM Watson Studio is great."
"IBM Watson Studio consistently automates across channels."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"I would like to see more features related to deployment."
"The model management features could be improved."
"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."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"I think maybe the support is an area where it lacks."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"The solution's interface is very slow at times."
"I want IBM's technical support team to provide more specific answers to queries."
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
"Some of the solutions are really good solutions but they can be a little too costly for many."
"So a better user interface could be very helpful"
"The initial setup was complex."
Earn 20 points
H2O.ai is ranked 21st in Data Science Platforms while IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews. H2O.ai is rated 7.6, while IBM Watson Studio 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 IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku, Microsoft Azure Machine Learning Studio and RapidMiner, whereas IBM Watson Studio is most compared with Databricks, Azure OpenAI, Microsoft Azure Machine Learning Studio, Google Vertex AI and SAS Visual Analytics. See our H2O.ai vs. IBM Watson Studio report.
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