From Visual Pipelines to Python Code in Driverless AI

This lesson walks through how the H2O platform supports different working styles when building AI models, from visual composition to writing code directly. It covers Driverless AI's standard UI, a guided wizard that asks about experience level and business goals, and the visualized pipeline diagram showing feature engineering and tuning steps after an experiment finishes. It shows that the underlying Python code for a model can be downloaded for retraining later, and that h2oGPTe agents write and preserve auditable Python code when analyzing data, so you can export, modify, and reuse it.