End-to-End AIOps Project on AWS EC2 | Prometheus, Grafana, Python & ML | Real-Time Monitoring

🔥 End-to-End AIOps Project on AWS EC2 In this video, I demonstrate how to build a complete AIOps monitoring solution from scratch using AWS, Prometheus, Grafana, Docker, Python, and Machine Learning. This project monitors CPU, Memory, and Network utilization in real time and uses Machine Learning to detect anomalies automatically. 📌 Technologies Used Git Repo : https://github.com/bdreddy738/aiops-m... ✅ AWS EC2 ✅ Linux ✅ Docker ✅ Prometheus ✅ Node Exporter ✅ Grafana ✅ Python ✅ Pandas ✅ Scikit-Learn ✅ Isolation Forest ✅ Git & GitHub 📌 Features ✔ Real-Time CPU Monitoring ✔ Real-Time Memory Monitoring ✔ Real-Time Network Monitoring ✔ Prometheus Metrics Collection ✔ Grafana Visualization ✔ Machine Learning Based Anomaly Detection ✔ End-to-End DevOps & AIOps Implementation ✔ Production-Style Monitoring Setup 📌 Project Flow 1. Launch AWS EC2 Instance 2. Install Docker 3. Configure Node Exporter 4. Configure Prometheus 5. Deploy Grafana 6. Collect CPU, Memory & Network Metrics 7. Build Python Monitoring Scripts 8. Train Machine Learning Model 9. Detect Infrastructure Anomalies 10. Visualize Results in Grafana 📌 Perfect For ✔ DevOps Engineers ✔ Cloud Engineers ✔ SRE Engineers ✔ Platform Engineers ✔ MLOps Engineers ✔ AWS Engineers ✔ Beginners Learning Monitoring If you found this video useful, please Like, Share and Subscribe. #AWS #DevOps #AIOps #Prometheus #Grafana #Python #MachineLearning #Docker #Monitoring #CloudComputing #SRE #MLOps #AWSProjects #devopsprojects 00:00 Introduction 01:00 Architecture Overview 03:30 AWS EC2 Setup 06:00 Docker Installation 10:00 Node Exporter Setup 15:00 Prometheus Configuration 22:00 Grafana Dashboard Setup 30:00 CPU Monitoring 35:00 Memory Monitoring 40:00 Network Monitoring 45:00 Python Integration 55:00 Machine Learning Model 01:05:00 Anomaly Detection Demo 01:10:00 GitHub Repository Walkthrough 01:15:00 Conclusion