We performed a comparison between Amazon SageMaker and H2O.ai 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 deployment is very good, where you only need to press a few buttons."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"The few projects we have done have been promising."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"Allows you to create API endpoints."
"We've had no problems with SageMaker's stability."
"We were able to use the product to automate processes."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The ease of use in connecting to our cluster machines."
"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 pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"Lacking in some machine learning pipelines."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"The documentation must be made clearer and more user-friendly."
"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."
"The solution is complex to use."
"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."
"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."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"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."
Earn 20 points
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while H2O.ai is ranked 20th in Data Science Platforms. Amazon SageMaker is rated 7.4, while H2O.ai is rated 7.6. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". 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". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and SAS Visual Analytics, whereas H2O.ai is most compared with Databricks, Dataiku, Microsoft Azure Machine Learning Studio, KNIME and IBM Watson Studio. See our Amazon SageMaker vs. H2O.ai report.
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