Overview

This bootcamp is built around industry level DevOps projects designed from real enterprise environments—not demo labs or YouTube-style examples. Every project you work on reflects how DevOps, Cloud, and Platform teams actually build, deploy, secure, and scale systems in real companies.

Unlike basic tutorials, these real time DevOps projects focus on end-to-end implementation—covering planning, automation, CI/CD, cloud governance, Kubernetes, monitoring, security, and AI integration. You don’t just follow steps; you understand why each tool and architecture is used in production.

Each live session simulates real workplace scenarios such as multi-account AWS setups, GitOps-based Kubernetes deployments, CI/CD security pipelines, cloud cost optimization, and MLOps workflows. This makes it easier to confidently explain your projects in interviews and showcase them as real experience on your resume.

By the end of the program, you will have hands-on experience with enterprise-grade DevOps and Cloud projects that align with roles like DevOps Engineer, Cloud Engineer, SRE, Platform Engineer, and AI Engineer.

✅ Project 1: DevOps Kickstart – Roadmap, Agile Practices & Team Workflow

Build a solid foundation for DevOps roles through Agile collaboration, project planning, and team workflow implementation.

Tech Stack: GitHub, Jira, Agile, CI/CD Concepts, Terraform Basics, Cloud Fundamentals.

✅ Project 2: Enterprise-Grade AWS Multi-Account Governance

Design and deploy a secure, scalable AWS multi-account architecture suitable for enterprise environments.

Tech Stack: AWS Control Tower, IAM, IAM Identity Center, SCPs, CloudTrail, AWS Organizations.

✅ Project 3: Golden AMI Pipeline with Packer & Terraform

Automate the creation and management of base images for EC2 deployments across multiple environments.

Tech Stack: Packer, Terraform, Bash Scripts, EC2, EBS Optimization, Canary Deployments.

✅ Project 4: Managing Hundreds of Servers at Scale with Ansible

Automate configuration management, application deployment, and orchestration across large-scale server fleets.

Tech Stack: Ansible, Dynamic Inventory (AWS EC2), SSH Hardening, Role-Based Playbooks.

✅ Project 5: Production-Ready Serverless AWS Infrastructure using Terraform

Design and deploy a scalable, secure, and cost-efficient AWS environment using Infrastructure as Code (IaC).

Tech Stack: Terraform Modules, AWS (VPC, ALB, RDS, Lambda, Python), Docker, KMS, CI/CD Integration.

✅ Project 6: GitHub Actions CI/CD Resilience and Security

Implement robust CI/CD pipelines that handle Terraform state, deployment automation, and security integrations for real-world DevOps environments.

Tech Stack: Terraform (State Management), GitHub Actions, Docker, Java/Maven, SonarQube, Snyk, JFrog Artifactory, Secret Management, Auto Scaling.

✅ Project 7: Infrastructure Automation with GitHub Actions

Create and manage complete infrastructure pipelines using GitHub Actions and self-hosted runners for scalable automation.

Tech Stack: GitHub Actions, EC2 Runners, Terraform, Packer, AMI Management, AWS CLI.

✅ Project 8: GitOps-Driven Kubernetes Deployment with Argo CD

Automate and manage Kubernetes applications at scale using GitOps workflows for continuous delivery and versioned infrastructure.

Tech Stack: EKS, Argo CD, Helm, Ingress, Kubernetes (Pods, Services, Autoscaling), GitHub.

✅ Project 9: Cloud Cost Optimization and Automation Strategy

Implement cloud cost optimization techniques to reduce expenses and improve efficiency. Automate resource management, including start/stop schedules and usage-based scaling, to achieve maximum savings.

Tech Stack: Python, AWS Cost Explorer, Billing Analysis, Resource Scheduling, GitHub, CPU Optimization.

✅ Project 10: Monitoring & Observability for Modern Cloud Infrastructure

Ensure system reliability through proactive monitoring, alerting, and blue-green deployment strategies. Learn to build an automated observability stack for production environments.

Tech Stack: Prometheus, Grafana, CloudWatch, JFrog, Ansible, Helm, Blue-Green Deployment Strategy.

✅ Project 11: MLOps for DevOps – Streamlining AI/ML Model Lifecycle

Bridge the gap between DevOps and MLOps by integrating AI/ML workflows into your automation pipelines. Implement MLOps best practices for continuous training, testing, and deployment using SageMaker.

Tech Stack: AWS SageMaker, MLOps Fundamentals, Model Deployment Strategies, Python, GitHub Actions.

✅ Project 12: End-to-End AI Model Deployment with Python & Containers

Build and deploy a complete AI model from scratch – train it using Python, containerize it, and deploy it to scalable infrastructure for real-world inference.

Tech Stack: Python, Scikit-learn/TensorFlow/PyTorch, Docker, Flask/FastAPI, EKS, GitHub.