Fast machine learning with H2O
Pros
- Excellent analytical and prediction tool
- In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
- Readily available algorithms, easy to use in your analytical projects
- Faster than Python scikit learn (in machine learning supervised learning area)
- It can be accessed (run) from Python, not only JAVA etc.
- Well documented and suitable for fast training or self studying
- In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
- It can be used as open source
Cons
- No weaknesses found yet
- This is not really a drawback, but rather a warning - the Drivereless AI is not a replacement for a data scientist yet, and will not replace data scientists in the next decade neither. The Driverless AI feature delivers reliable results only if the analyst is sure about the meaning of input data. The data quality is usually a major issue and no tool can detect the meaning of data in the input. Data scientists are also required for business interpretation of the findings. So be careful, and do not rely on this feature without a good understanding of what it really does in each step.
Return on Investment
- By using H2O the analyst can focus on analysis itself, not spend too much time with coding etc.
- Reuse of algorithms and easy model sharing saves time and money
- An easy learning curve assures low training costs
- By moving to a paid version, even the Driverless AI, you will still need data scientists and analysts, but maybe not so many!
Alternatives Considered
TensorFlow
