AI Anomaly Detection
Build an anomaly detection system that monitors time-series metrics, detects unusual patterns using statistical and ML methods, triggers alerts, and provides AI-powered root cause analysis.
"API latency spiked 3x at 14:32 — the root cause is a slow database query from the new deployment"
6 Modules
Each module builds on the previous one. By the end, you have a complete production system.
- 1
Data Ingestion
Multi-source metric pipeline with windowing
- 2
Statistical Baselines
Moving averages, z-scores, seasonal decomposition
- 3
ML Detection
Isolation forest + autoencoder ensemble
- 4
Alert Engine
Rules, dedup, escalation, notification
- 5
Monitoring App
Next.js dashboard with anomaly timeline
- 6
Production Pipeline
Root cause analysis + SLA tracking
Production patterns you'll master
Synthetic data included
- API latency metrics (30 days)
- Error rate logs (JSON)
- Deployment events
- Infrastructure metrics (CPU/memory)
- Incident history
What you walk away with
Shareable portfolio
A public URL showing your module timeline, patterns mastered, and completion status.
All the code
Download everything as a ZIP — pipelines, guardrails, deployment configs. Yours forever.
Module walkthrough
Each module documented with deliverables and the production pattern you implemented.