AWS DevOps & Cloud
Cloud Infrastructure & Automation
Comprehensive cloud infrastructure with automated development practices using AWS CodePipeline, EC2, S3, and AWS CDK for scalable applications. Production-ready CI/CD pipelines and infrastructure-as-code implementations ensure reliability, security, and rapid delivery across environments.
Projects Showcase
Real-world implementations demonstrating production-ready AWS DevOps solutions
Console UI
AWS DevOps CI/CD Pipeline
Complete CI/CD pipeline implementation using AWS CodePipeline, CodeBuild, and CodeDeploy for automated build, test, and deployment workflows with GitHub as single source of truth.
- AWS CodePipeline → CodeCommit → CodeBuild → CodeDeploy with rollback option
- EC2 instances management with S3 bucket roles and policies
- BuildSpec scripting with key-value pairs for automation
- Manual approval stages with automated SNS notifications
- CloudWatch for comprehensive logging and monitoring
K8s
Amazon EKS Managed Stack
Deployment of machine learning models on Amazon EKS cluster using Docker containers with CloudFormation infrastructure provisioning.
- Infrastructure deployed via CloudFormation using EKSCTL CLI
- ML Model deployment on EKS cluster using KUBECTL CLI
- Service cluster/IP configuration with load balancer setup
- Endpoint invocation for prediction (local system & Postman testing)
- AWS managed control-plane with configurable worker nodes
Own Stack
AWS CDK Programmatic Stack
Programmatic cloud infrastructure deployment using AWS CDK Toolkit for reusable, scalable infrastructure-as-code solutions.
- CDK Bootstrap → CDK Synth (Python/JavaScript) → .yaml files → CDK Deploy
- App.py and stack.py programmatic infrastructure definition
- CodeBuild PipelineProject with BuildSpec.from_Object → S3 artifacts
- Multi-stage pipeline: Source → Build → Deploy with GitHub integration
- SecretValue.secrets_manager() for secure credential management
MLOps
AWS ECS Fargate MLFlow
MLFlow tracking server deployment on AWS Fargate with complete infrastructure automation and containerized MLOps workflows.
- CDK Toolkit (IaC) → CloudFormation → ELB (traffic controller)
- ECS-Fargate (auto-scalable) with S3 bucket storage
- RDS for metadata/tables storage with IAM roles/policies
- VPC networking with SageMaker AI integration
- EC2-ECR for training code image management
ML
AWS SageMaker AI
End-to-end machine learning workflow with model building, training, and deployment using AWS SageMaker with real-time inference endpoints.
- Notebook instances (Jupyter) with IAM roles for S3/ECR access
- ML training service: training and hyperparameter tuning (.tar files)
- ML hosting services: auto-scaling, low-latency endpoints
- MLFlow integration for experiment tracking and model registry
- Comprehensive logging and monitoring: latency, error rates
