Fine-Tuning

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.

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/baseline with a machine_id
Canary Edge computes energy statistics and trains a lightweight predictor (~462K params) in seconds
Future detection calls with that machine_id use the fine-tuned model automatically
Detection accuracy typically improves from ~82% (generic) to 97%+ (fine-tuned)

Setting a Baseline

curl -X POST https://api.canaryedge.com/v1/baseline \
  -H "Content-Type: application/json" \
  -H "Ocp-Apim-Subscription-Key: YOUR_API_KEY" \
  -d '{
    "machine_id": "pump-assembly-1",
    "energies": [0.12, 0.15, 0.11, 0.13, 0.14, 0.12, 0.16, 0.11, ...]
  }'

import requests

# Step 1: Collect energy scores from normal operation
energies = []
for window in normal_operation_windows:
    resp = requests.post(
        "https://api.canaryedge.com/v1/detect",
        headers={"Ocp-Apim-Subscription-Key": "YOUR_API_KEY"},
        json={"machine_id": "pump-assembly-1", "series": window}
    )
    energies.append(resp.json()["energy"])

# Step 2: Set the baseline
requests.post(
    "https://api.canaryedge.com/v1/baseline",
    headers={"Ocp-Apim-Subscription-Key": "YOUR_API_KEY"},
    json={"machine_id": "pump-assembly-1", "energies": energies}
)

Only send energy scores from normal operating data for baseline creation. Including anomalous data will degrade detection accuracy.

Checking a Baseline

curl "https://api.canaryedge.com/v1/baseline?machine_id=pump-assembly-1" \
  -H "Ocp-Apim-Subscription-Key: YOUR_API_KEY"

Response:

{
  "machine_id": "pump-assembly-1",
  "mean": 0.132,
  "std": 0.018,
  "p99": 0.187,
  "sample_count": 500,
  "created_at": "2026-03-15T10:00:00Z"
}

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

Machine Status

Once a baseline is set, check machine status:

curl "https://api.canaryedge.com/v1/status/pump-assembly-1" \
  -H "Ocp-Apim-Subscription-Key: YOUR_API_KEY"

Deleting a Machine

To remove a baseline and revert to the generic detector:

curl -X DELETE "https://api.canaryedge.com/v1/machines/pump-assembly-1" \
  -H "Ocp-Apim-Subscription-Key: YOUR_API_KEY"

Getting Started

Guides

API Reference

Azure-Compatible Endpoints

Native Endpoints

System

When to Set a Baseline

How It Works

Setting a Baseline

Checking a Baseline

Requirements

Machine Status

Deleting a Machine

​When to Set a Baseline

​How It Works

​Setting a Baseline

​Checking a Baseline

​Requirements

​Machine Status

​Deleting a Machine

When to Set a Baseline

How It Works

Setting a Baseline

Checking a Baseline

Requirements

Machine Status

Deleting a Machine