🎯 Sprint-7 Pre-Release: RCA, Alert Correlation, and Incident Summary Pipeline

This sprint focuses on building a complete Incident Management Pipeline that performs:

✅ Features Included

• 🔗 Alert correlation across multiple services
• 🛠️ Root cause analysis (RCA) using log and metric patterns
• 📋 Incident summarization with timeline and severity context
• 📦 Outputs clean incident_summary.json for downstream tools
• 📂 Modularized design under core/ with clean structure for extendability

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🧠 Sample RCA Flow

Given alerts like:

• High latency in auth-service
• Memory spike in auth-service
• High CPU in payments-service

The system produces:

• 🔗 Correlated Events
• 🛠️ Root Cause List (service-wise)
• 📋 Full Markdown-like Incident Summary
• 💾 JSON written to /output/incident_summary.json

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🚀 How to Run

cd sprint-7/
python3 main.py

Sprint-6: Auto-Remediation, Smart Alert Routing, Log Triage

Sprint-6 Pre-Release Summary

Sprint 3 and 4: Drift Monitoring & Detection

🚀 Features in this Release

Sprint-3: PSI Drift API

• Added sprint-3/api/psi_drift.py with Population Stability Index (PSI) computation.
• REST API for monitoring prediction distribution drift.

Sprint-4: Model Drift Detection

• Integrated statistical tracking for model performance drift.
• Placeholder for A/B metrics, alerts, and baseline versioning logic.

🎯 Ready for integration with upstream monitoring & model registry pipelines.

Sprint-2: FastAPI Inference API

Add what Sprint-2 includes (e.g., FastAPI-based inference API, schema validation, run.py launch script, proper modular import, etc.)

Sprint-1 Release (Pre-Release)

🚧 Pre-Release: Sprint-1 Deliverables

• ✅ Dummy transaction data generation
• ✅ Model training with schema validation
• ✅ Inference pipeline with Pydantic
• ✅ Unit test using pytest
• ✅ Model: autopilot_clf.pkl
• ✅ Folder structure under sprint-1/

Notes:

• This is an early milestone.
• Model/inference subject to change in future sprints.

Sprint-5: Monitoring Stack Release

Includes Grafana dashboards, Prometheus configs, latency/error metrics, CPU monitoring, alert simulations, and 15 production-level monitoring scenarios.