We performed a comparison between H2O.ai and Saturn Cloud based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."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."
"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."
"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."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"Saturn Cloud supports GPU as part of the environment, which is essential for many computational tasks in machine learning projects. It also allows us to edit the environment, including the image, before we start the cloud resources. This feature lets us quickly set up the environment without the hassle of moving the data and code to another cloud device."
"The feature I like the most about Saturn Cloud is that it has lightning-fast CPUs."
"There is plenty of computational resources (both GPU, CPU and disk space)."
"It didn't take long to see that Saturn Cloud could scale with my needs, providing more resources when required."
"It offered an excellent development environment while not touching our production cloud resources."
"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."
"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."
"The model management features could be improved."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"Public Clouds integration and sandbox environments would be a true game changer."
"Providing more detailed and beginner-friendly documentation, especially for advanced features, could greatly enhance the user experience."
"We'd like to have the capability for installing more libraries."
"Saturn Cloud should include prebuilt images for advanced data science packages like LightGBM in the next release. If possible, they should also provide a Kaggle image, which contains the most common Python packages used in machine learning."
"It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs."
Earn 20 points
H2O.ai is ranked 20th in Data Science Platforms while Saturn Cloud is ranked 8th in Data Science Platforms with 5 reviews. H2O.ai is rated 7.6, while Saturn Cloud is rated 10.0. 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 Saturn Cloud writes "Great support, good availability, and seamless integration capabilities". H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku, Microsoft Azure Machine Learning Studio and KNIME, whereas Saturn Cloud is most compared with Amazon SageMaker and Remote Desktop with Multi-user support by Aurora.
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