Vigil watches your ML experiments 24/7. It detects anomalies, suggests optimizations, and stops wasted GPU hours before they happen.
Current ML tools are glorified chart viewers. You train a model, check the graphs, spot a problem hours later, and re-run. That loop costs time and compute. Vigil breaks it by watching every metric in real-time and intervening the moment something goes wrong.
Not another dashboard to check. An AI teammate that handles ML operations autonomously.
Detects gradient explosions, loss plateaus, data drift, and overfitting in real-time. Acts before wasted compute, not after.
Suggests or auto-applies learning rate adjustments, batch size changes, and early stopping based on training trajectory analysis.
Auto-generates run comparisons, predicts final metrics before training completes, and surfaces the best-performing configurations.
Tracks GPU spend per experiment, kills failing runs early, and estimates cost-to-completion so you never burn budget on a dead end.
| Capability | Traditional Tools | Vigil |
|---|---|---|
| Anomaly detection | You spot it manually | Caught and handled automatically |
| Hyperparameter tuning | Grid search, then wait | Adjusted mid-training based on trajectory |
| Experiment comparison | Side-by-side charts | AI-generated analysis with recommendations |
| Cost management | Check cloud billing after | Real-time spend tracking, auto-stop on waste |
| When it works | When you're looking at it | 24/7, including while you sleep |
The future of ML operations isn't better dashboards. It's AI that understands your experiments deeply enough to run them alongside you.