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.
H2O.ai provides fast training and memory-efficient DataFrame manipulation, benefiting those transitioning from Spark. Integration with enterprise Java apps via POJO/MOJO enhances usability. AutoML facilitates model evaluation while driverless capability allows effective algorithm assessment. It supports Jupyter Notebooks and flexible model exploration, yet trails R and Pandas in data manipulation. Enhancements are needed in integration with systems like SageMaker, model management, multimodal support, and data source compatibility.
