Cloud Cost Anomaly and FinOps Dashboard
Multi-cloud billing telemetry collector with machine learning cost-spike detection and unattached resource cleanup recommendations.
Project Overview
Ingests CloudWatch and GCP billing metrics, applies Isolation Forest models to detect sudden cost anomalies, and highlights idle EC2 instances, orphaned EBS volumes, and oversized database clusters to optimize cloud spend. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for Cloud Cost Anomaly and FinOps Dashboard.
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.