Please share with the community what you think needs improvement with H2O.ai.
What are its weaknesses? What would you like to see changed in a future version?
On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time. It becomes a problem. I would like to see better integration with Python and data science capabilities.
I would like to see more features related to deployment.
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 more support for scalability and deep learning. Right now, they are very strong in supervise and supervise learning, but not in deep learning. I'd like to see them be more well-rounded, where they have support for deep learning, but I'm not sure that is their business model.
The model management features could be improved.