Canary Edge ships with a powerful generic model that works out of the box. For production workloads, setting a baseline for each machine fine-tunes a dedicated predictor that learns the specific patterns of your equipment.Documentation Index
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When to Set a Baseline
- Your time series has unique seasonal patterns
- The generic model produces too many false positives
- You need maximum sensitivity for critical equipment
- You want regime classification (HEALTHY/ACTIVE/TRANSITION/SHOCK)
How It Works
- Send normal operating data via
POST /v1/baselinewith amachine_id - Canary Edge computes energy statistics and trains a lightweight predictor (~462K params) in seconds
- Future detection calls with that
machine_iduse the fine-tuned model automatically - Detection accuracy typically improves from ~82% (generic) to 97%+ (fine-tuned)
Setting a Baseline
Checking a Baseline
Requirements
- Minimum 100 energy values for a reliable baseline
- Data should represent at least one full operational cycle
- All data should be from normal, healthy operation