We performed a comparison between H2O.ai and IBM Watson Studio 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."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The system's ability to take a look at data, segment it and then use that data very differently."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"It is a stable, reliable product."
"It has a lot of data connectors, which is extremely helpful."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"Watson Studio is very stable."
"The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video."
"I would like to see more features related to deployment."
"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."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"The model management features could be improved."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"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."
"The solution's interface is very slow at times."
"The main challenge lies in visibility and ease of use."
"The initial setup was complex."
"So a better user interface could be very helpful"
Earn 20 points
H2O.ai is ranked 19th 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 Data Science Studio, Microsoft Azure Machine Learning Studio and RapidMiner, whereas IBM Watson Studio is most compared with Databricks, Microsoft Azure Machine Learning Studio, Azure OpenAI and Google Vertex AI.
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