Most ML models decay over time.
Data changes. Relationships shift. Performance drops silently.
That's something I kept noticing while going through ML tutorials. Almost all of them end at the same point: "My model hits 95% accuracy!" And then… nothing.
Nobody talks about what happens three months later when user behavior changes, inputs drift, and that same model quietly slides to 70% accuracy while still running in production.
That gap bothered me.
So I'm building a system that monitors models 24/7, catches problems early, and fixes itself without manual intervention.
Monitors incoming data for distribution changes
Detects when model performance drops
Retrains automatically on fresh data
Validates new models before deployment
Deploys gradually with zero downtime
| Category | Technology | Purpose |
|---|---|---|
| Training & Experiments |   |
model training |
| Training & Experiments |  |
experiment tracking, model versioning |
| Automation |  |
schedules drift checks and retraining |
| Automation |  |
containerization |
| Serving |   |
prediction API |
| Monitoring |  |
dashboards |
| Monitoring |  |
alerts |
| Deployment |  |
CI/CD |