Volume-based anomaly detection for early issue identification — seasonal baselines, z-score detection, service health correlation, and incident insight reporting
-
Updated
Jul 2, 2026 - Python
Volume-based anomaly detection for early issue identification — seasonal baselines, z-score detection, service health correlation, and incident insight reporting
Client timesheet and billable hours tracking application
Test repo created by Devin
EventFlow Devin Integration — Azure Function alert webhook, MCP server for Log Analytics, shared schemas, demo runbook
EventFlow Order Service — System 1: FastAPI order API + Azure Service Bus event publisher for event-driven architecture demo
Customer-facing e-commerce storefront for EventFlow demo. Workshop participants experience the zero-decimal currency bug.
EventFlow Payment Service — System 2: event consumer + payment processor with zero-decimal currency bug for incident response demo
Automated remediation of pod failures after credential rotations — K8s monitoring agents, ServiceNow approval workflow, and orchestrated pod restart
Self-healing data pipeline — Kafka/Spark/Snowflake ingestion with LangChain decision agent for anomaly detection (schema drift, late arrivals, DQ failures), autonomous remediation (retrigger, schema remap, quarantine), and incident tracking. Source: AbdulSohail018/Autonomous-Orchestrator-Ai (MIT)
Add a description, image, and links to the observability-sre topic page so that developers can more easily learn about it.
To associate your repository with the observability-sre topic, visit your repo's landing page and select "manage topics."