Operations & Reliability

System health, automated recovery, and enterprise-grade alerting

Operations

What this does: Monitors system health 24/7 and automatically alerts on anomalies — providing enterprise-grade uptime and rapid incident response to keep forecasts flowing without interruption.

The system is engineered for continuous reliability, providing automated monitoring, lifecycle management, and resilient failure recovery for mission-critical demand forecasts.

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Model Lifecycle Management

The system automatically manages the transition of models through the MLOps lifecycle, ensuring only validated models reach the production environment.

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Model Promotion

1. Candidate Selection

   Identifies the latest training run artifact (regressor_YYYYMMDD.pickle) from the registry.

2. Validation

   Compares performance metrics against the current production baseline.

3. Promotion

   Updates the production alias in MinIO and MLflow for seamless serving switchover.

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Artifact Registry

All models and metadata are persistently stored in MinIO (S3) and PostgreSQL, providing a full audit trail of every prediction ever served.

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System Resilience

The platform is designed for graceful degradation, ensuring forecasts are delivered even if secondary services are offline.

Show Isolated Failure Domains

Failures in experiment tracking (MLflow) or hyperparameter storage (Optuna) do not block core training or inference.

Show Graceful Fallback

Use pre-validated stable configurations if the optimization engine is unreachable.

Show Idempotency

All operations are idempotent, allowing for safe retries and self-healing.

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Production Alerting

To support rapid incident response, the platform utilizes structured HTML email notifications for immediate triage and visibility.

• Automated Alerts: Real-time notifications for pipeline failures, data drift, or service outages.
• Context-Rich Content: Includes failure severity, model versions, run IDs, and direct links to Airflow/MLflow logs.
• Triage Safety: Designed to separate actionable signals from noisy warnings, ensuring the MLOps team can respond to critical events immediately.

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Monitoring & Drift Detection

We compare live data against the training baseline to detect Data Drift.

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Drift Metrics

Show PSI (Population Stability Index)

Threshold: > 0.2

Measures major shifts in overall population distribution.

Show Wasserstein Distance

Threshold: > 0.1

Sensitive to changes in distribution shape and outliers.

Automated Recovery: The retraining scheduler initiates self-healing if drift is detected or performance decays below thresholds.

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Infrastructure & Logging

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Artifact Storage

The create_log_bucket.py script ensures our MinIO artifact store is initialized.

Idempotency: The script checks BucketAlreadyOwnedByYou to prevent errors on restart or redeployment.

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Centralized Logging

Centralized logging configuration (logging_config.py) suppresses noise from secondary libraries (Kedro, Great Expectations), ensuring that the MLOps team can focus on proprietary application signals and critical system events.

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Validation Logic

Drift and retraining logic are validated using standard pytest suites:

# Analysis from test_production_ops.py
def test_drift_trigger(self):
    detector = DriftDetector(reference_data)
    drift_score = detector.calculate_psi(current_data)
    assert drift_score > 0.2  # Critical threshold