ML Experiment Tracking in H2O Driverless AI | Part 10

Now let's talk about experiment tracking. Before our models are in production, we have experiments, lots of experiments. In tracking these experiments, comparing the results and learning what works is fundamental to data science. First, parallel experiments. You can run multiple experiments simultaneously with automated resource utilization and queuing. Maybe you're testing a baseline with minimal settings versus a maxed out accuracy and time versus custom recipes from your data engineering team. The platform schedules and manages these parallel runs efficiently. Every experiment captures complete configuration, the data set, target variable, weight column if you're using one, validation strategy, accuracy settings, and so forth. These configurations are versioned and reproducible. During execution, you can watch the experiments in real time. The evolving leaderboard shows which models are performing best. Feature engineering is progressing. Cross validation scores. This is all live and the visibility means you're not just submitting jobs and hoping. You could see the model development process. Once experiments complete, comparison becomes critical. We provide side-by-side comparisons of experiments with visualizations of performance metrics and statistical summaries. You can see experiment Achieved a higher validation, but maybe experiment B was easier to interpret. All experiments are automatically synced to Amalops based on the workspace that they're in. This makes it so 6 months from now, someone can look back and see what we've already tried and that the approach maybe didn't work. And here's why. Everything we're talking about in the UI is accessible via API as well. You can programmatically query experiments, extract metrics, generate custom reports, or integrate experiment tracking into your own CI/CD pipelines and workflow orchestration tools.