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. It also gives you an interpretability capability which allows you to have some understanding of what's inside the algorithm and why it's behaving a certain way, making sure you are not bias towards the outcome.

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.

• Fast training
• Memory-efficient DataFrame manipulation
• Well-documented, easy-to-use algorithms
• Ability to integrate with enterprise Java apps (through POJO/MOJO) 

These are the main reasons why we switched from Spark to H2O.

AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms; with training input data.

Still on it.

The ease of use in connecting to our cluster machines.