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GET
/
monitoring
/
drift
Drift Monitoring
curl --request GET \
  --url https://api.example.com/monitoring/drift
{
  "samples_observed": 123,
  "drift_score_max_abs_z": 123,
  "drifted_features": [
    {}
  ],
  "predicted_positive_rate": 123,
  "training_positive_rate": 123,
  "should_retrain": true,
  "reason": "<string>",
  "recommended_action": "<string>"
}

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/RaviTejaMedarametla/Data-Science-AI-Portfolio/llms.txt

Use this file to discover all available pages before exploring further.

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Model Drift Status

Monitors feature distribution drift and prediction rate shifts to detect when model retraining is needed.

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Endpoints

GET /monitoring/drift
GET /monitoring/retraining_trigger
Both endpoints return identical responses. The /monitoring/retraining_trigger path is an alias for semantic clarity in retraining workflows.

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Response

Returns comprehensive drift analysis based on accumulated prediction data.
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samples_observed
integer
required
Total number of predictions made since service startup.
​
drift_score_max_abs_z
number
required
Maximum absolute z-score across all monitored features.Measures how many standard deviations the current feature means are from training baseline.
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drifted_features
array
required
List of feature names that have drifted beyond the z-score threshold.Empty array if no features have drifted significantly.
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predicted_positive_rate
number
required
Current rate of positive predictions (predicted_purchase=1) in production.Value between 0.0 and 1.0.
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training_positive_rate
number
required
Historical positive rate from the training dataset baseline.Used to detect prediction rate shift.
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should_retrain
boolean
required
Whether retraining is recommended based on drift detection rules.
​
reason
string
required
Human-readable explanation for the retraining recommendation.Possible values:
  • baseline_not_loaded - Drift baseline file missing
  • no_predictions_observed - No predictions made yet
  • insufficient_samples - Need more samples for reliable drift detection
  • below_threshold - No significant drift detected
  • feature_distribution_drift - Features have drifted significantly
  • prediction_rate_shift - Prediction rate has changed significantly
​
recommended_action
string
required
Specific action to take based on drift status.Possible values:
  • run_training_to_generate_baseline - Run training pipeline to create baseline
  • collect_inference_samples - Continue collecting prediction data
  • collect_more_samples - Need more data for drift analysis
  • continue_monitoring - Keep monitoring, no action needed
  • trigger_retraining_pipeline - Initiate model retraining

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Status Codes

  • 200 OK - Drift status computed successfully

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Example Request

cURL
curl -X GET "http://localhost:8000/monitoring/drift" \
  -H "accept: application/json"

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Example Responses

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No Drift Detected

200 OK
{
  "samples_observed": 1247,
  "drift_score_max_abs_z": 1.8342,
  "drifted_features": [],
  "predicted_positive_rate": 0.3456,
  "training_positive_rate": 0.3512,
  "should_retrain": false,
  "reason": "below_threshold",
  "recommended_action": "continue_monitoring"
}

​
Feature Drift Detected

200 OK
{
  "samples_observed": 2834,
  "drift_score_max_abs_z": 4.2187,
  "drifted_features": [
    "minutes_watched",
    "practice_exams_started"
  ],
  "predicted_positive_rate": 0.2891,
  "training_positive_rate": 0.3512,
  "should_retrain": true,
  "reason": "feature_distribution_drift",
  "recommended_action": "trigger_retraining_pipeline"
}

​
Prediction Rate Shift

200 OK
{
  "samples_observed": 1523,
  "drift_score_max_abs_z": 2.1456,
  "drifted_features": [],
  "predicted_positive_rate": 0.4789,
  "training_positive_rate": 0.3512,
  "should_retrain": true,
  "reason": "prediction_rate_shift",
  "recommended_action": "trigger_retraining_pipeline"
}

​
Insufficient Data

200 OK
{
  "samples_observed": 42,
  "drift_score_max_abs_z": 2.8921,
  "drifted_features": ["courses_started"],
  "predicted_positive_rate": 0.4286,
  "training_positive_rate": 0.3512,
  "should_retrain": false,
  "reason": "insufficient_samples",
  "recommended_action": "collect_more_samples"
}

​
Baseline Not Loaded

200 OK
{
  "samples_observed": 0,
  "drift_score_max_abs_z": 0.0,
  "drifted_features": [],
  "predicted_positive_rate": 0.0,
  "training_positive_rate": 0.0,
  "should_retrain": false,
  "reason": "baseline_not_loaded",
  "recommended_action": "run_training_to_generate_baseline"
}

​
Implementation Details

Defined in src/api.py:300-302 Response Model: DriftStatusResponse (src/api.py:56-64) 
class DriftStatusResponse(BaseModel):
    samples_observed: int
    drift_score_max_abs_z: float
    drifted_features: List[str]
    predicted_positive_rate: float
    training_positive_rate: float
    should_retrain: bool
    reason: str
    recommended_action: str

​
Drift Detection Algorithm

Implemented in _compute_drift_status() (src/api.py:91-172)

​
Configuration Parameters

Defined in config.yaml under the monitoring section: 
monitoring:
  drift_min_samples: 100              # Minimum samples needed for drift analysis
  drift_zscore_threshold: 3.0         # Z-score threshold for feature drift
  drift_min_features: 2               # Minimum drifted features to trigger retraining
  class_rate_shift_threshold: 0.10    # Prediction rate shift threshold (10%)

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Detection Logic

  1. Feature Drift Detection
    • For each numeric feature, compute running mean from predictions
    • Calculate z-score: (current_mean - baseline_mean) / baseline_std
    • Flag feature as drifted if |z-score| >= drift_zscore_threshold
    • Trigger retraining if drifted_features.count >= drift_min_features
  2. Prediction Rate Shift Detection
    • Track ratio of positive predictions in production
    • Compare against training dataset positive rate
    • Trigger retraining if |predicted_rate - training_rate| >= class_rate_shift_threshold
  3. Sample Size Validation
    • Require at least drift_min_samples before analyzing drift
    • Prevents false positives from small sample noise

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Thread Safety

Monitoring state is protected by a threading lock (_LOCK) to ensure atomic updates:
  • Feature sums accumulated after each prediction
  • Sample counts incremented atomically
  • Positive prediction counts tracked accurately

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Baseline Generation

The drift baseline is generated during model training (src/train.py):
  1. Compute statistics (mean, std) for all numeric features in training data
  2. Calculate training set positive rate
  3. Save to artifacts/drift_baseline.json
  4. Loaded at API startup from config path: artifacts.drift_baseline_file
Baseline File Location: artifacts/drift_baseline.json

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Integration with MLOps Pipeline

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Automated Retraining Workflow

import requests
import time

while True:
    response = requests.get("http://api:8000/monitoring/drift")
    status = response.json()
    
    if status["should_retrain"]:
        print(f"Drift detected: {status['reason']}")
        print(f"Action: {status['recommended_action']}")
        # Trigger retraining pipeline
        trigger_training_job()
        time.sleep(86400)  # Check daily
    else:
        time.sleep(3600)  # Check hourly

​
Monitoring Dashboard Metrics

Recommended dashboard visualizations:
  • Time series of drift_score_max_abs_z
  • Prediction rate vs training rate comparison
  • Count of drifted features over time
  • should_retrain flag alerts
  • Health Check - Check if drift baseline is loaded
  • Predict - Single predictions that contribute to drift tracking
  • Batch Predict - Batch predictions that contribute to drift tracking

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Best Practices

  1. Monitor regularly - Check drift status hourly or daily depending on prediction volume
  2. Adjust thresholds - Tune drift_zscore_threshold and class_rate_shift_threshold based on your model’s stability
  3. Validate retraining - Always validate retrained models before deployment
  4. Reset monitoring - Clear monitoring state after retraining by restarting the service
  5. Log drift events - Integrate drift alerts with your observability platform

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