Tracking and Comparing ML Experiments in H2O
- Observability
- H2O Driverless AI
- H2O MLOps
This lesson walks through how the platform runs multiple experiments in parallel with automated resource management, letting a team compare a minimal baseline against a maxed out accuracy run or a custom recipe at the same time. It shows how every experiment captures a versioned, reproducible configuration covering the dataset, target, validation strategy, and accuracy settings, with a live leaderboard for watching cross validation scores as training progresses. The lesson also covers side by side comparison of experiments and how each one syncs automatically to H2O MLOps so results stay queryable later through the API.
Key takeaways
- How running experiments in parallel lets a team compare a baseline against a maxed out accuracy run at once
- What gets captured in a versioned experiment configuration, from the dataset to the validation strategy
- Why side by side comparison can reveal one experiment scores higher while another is easier to interpret
- How every experiment automatically syncs to H2O MLOps so results stay queryable through the API later
Start owning your AI