CompletedGoAWS CloudWatchDocker+3 more
CloudPulse-AI
Go-based observability system for AI workloads — tracks model drift, latency, and system health with 92% drift-detection accuracy and fully automated deployments.
Timeline
Ongoing
Role
DevOps / Platform Engineer
Team
Solo
Status
CompletedTechnology Stack
Go
AWS CloudWatch
Docker
GitHub Actions
Terraform
CloudFormation
Key Challenges
- Detecting model drift reliably in production
- Designing low-overhead metric collection in Go
- Keeping alerting actionable without noise
Key Learnings
- Observability patterns for AI workloads
- Go for systems tooling
- Infrastructure-as-code with Terraform and CloudFormation
CloudPulse-AI: Cloud & Drift Monitoring
Overview
CloudPulse-AI is a Go-based observability system for AI workloads. It tracks model drift, latency, and system health, using Isolation Forest anomaly detection to reach 92% drift-detection accuracy — catching distribution shifts before they degrade production models.
Features
- Real-time monitoring of model drift, latency, and system health
- Isolation Forest anomaly detection (92% accuracy)
- Centralized logging and alerting through AWS CloudWatch — 70% faster incident response
- Automated deployments with Docker and GitHub Actions — 30% faster deployment cycles
- Infrastructure managed as code with Terraform and CloudFormation
Architecture
A lightweight Go agent collects model and system metrics and publishes them to CloudWatch. A drift-detection layer flags anomalies in model inputs and outputs, with alerting wired to keep incident response fast and low-noise.