Build an AI-Powered Incident Analyzer | FastAPI + GenAI + AWS | Real-World Project

Description

In this video, we build a real-world AI-powered Incident Root Cause Analyzer using FastAPI, Generative AI, and AWS. In modern production systems, engineers receive massive and unstructured log files during outages. Manually analyzing these logs is slow, error-prone, and heavily dependent on experience. To solve this, we design an AI-based system that: - Automatically analyzes production incident logs - Identifies root cause and severity - Suggests immediate fixes and long-term prevention - Uses multiple GenAI models with clear responsibilities - Runs as a production-ready FastAPI service - Is containerized using Docker and deployed on AWS 🧠 Tech Stack Used: - FastAPI - Uvicorn - LangChain & LangServe - Google Gemini API - Groq (LLaMA) - Docker - AWS EC2 This project demonstrates how AI can be used in DevOps and SRE workflows (AIOps) to reduce downtime and improve incident response time. If you are a GenAI Engineer, DevOps Engineer, SRE, or Backend Developer, this project is perfect for your portfolio and interviews. 📌 Topics Covered: - Real-world problem statement - AI system architecture - FastAPI backend design - Multi-model GenAI orchestration - Dockerization - AWS deployment - Production-ready best practices 👍 Like the video if you found it useful 🔔 Subscribe for more real-world GenAI & system design projects #GenAI #FastAPI #DevOps #AIOps #AWS #Docker #BackendEngineering