MLOps Pipelines — Docs

This is where most industrial AI projects die. The pilot works, everyone celebrates, and six months later the model has drifted into irrelevance because nobody maintained it. SUPi's MLOps pipelines prevent that by design.

Model Lifecycle

Training → Validation → Deployment → Monitoring → Retraining
    ↑                                                    │
    └────────────── Continuous Loop ─────────────────────┘

Key Features

Automated retraining — triggered by drift detection or schedule
Model versioning — full history with rollback capability
A/B testing — compare new models against production before promoting
Performance dashboards — accuracy, latency, and drift metrics
Alerting — notifications when model performance degrades

Drift Detection

SUPi monitors for three types of drift:

Data drift — input distributions shift (e.g. seasonal changes, new operating modes)
Concept drift — the relationship between inputs and outputs changes (e.g. after equipment overhaul)
Performance drift — prediction accuracy degrades over time

When drift is detected, the pipeline automatically initiates retraining with the latest data while keeping the current model in production.