Introduction Running production-grade Kubernetes clusters requires resiliency, scalability, and automation. In this project, I built a self-healing Amazon EKS cluster using Terraform and Cluster Autoscaler, demonstrating how infrastructure can automatically recover and scale based on workload demands. The project highlights: Automated provisioning of VPC, EKS cluster, and node groups using Terraform Dynamic scaling of worker nodes with Cluster Autoscaler Resilient workloads that self-heal when nodes fail or pods are evicted Spot and on-demand instance management for cost optimization This project showcases a production-ready cloud infrastructure workflow suitable for portfolios targeting Cloud, Kubernetes, or DevOps roles. ...
How I Completed the Cloud Resume Challenge (And What I Learned)
Introduction In this project, I built a production-ready cloud resume website using AWS S3, Route 53, Lambda, DynamoDB, Terraform, and GitHub Actions. The goal wasn’t just to deploy a static website—it was to tackle real-world cloud challenges, including IAM permission errors, API Gateway CORS issues, Terraform quirks, and automated CI/CD deployments. This blog is a step-by-step technical walkthrough, highlighting the architecture, the challenges I faced, and the lessons I learned along the way. ...
Plant Pal: Building a Smart MLOps-Powered Plant Health Classifier
Introduction Plant Pal is a machine learning project that helps gardeners and plant enthusiasts identify the health status of plants from images. The project is more than just training a model—it’s a complete MLOps workflow demonstrating: End-to-end pipeline orchestration Experiment tracking and model versioning Deployment of a web app for real-time predictions Technologies Used: Python, TensorFlow/Keras, ZenML, MLflow, Streamlit Motivation: Showcase a production-ready ML workflow and MLOps skills relevant to Cloud/MLOps roles. ...