(Zoom-Based Live Sessions - Unmute Anytime to Ask Questions)
Ideal for professionals aiming for roles like DevOps Engineer, Cloud Engineer, Platform Engineer, SRE, AI Engineer, or Cloud Architect.
These are not YouTube-style mini-projects - every session is built on real-world, enterprise-grade use cases, simplified for better understanding
Basic knowledge of DevOps and Cloud technologies is required.
Each project is taught as a live, independent session - so you can join anytime and catch up cyclically.
Program Duration: 12 Weeks
Mode: Live on Zoom - fully interactive with screen sharing and Q&A.
📌 Project Sessions
📌 Doubt-Clearing Sessions
💰 Fees cover the entire program, including all materials, recordings, and continuous support.
Register directly via this Topmate Page (available worldwide).
Backup payment option for India users:
✅ 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.
📍 Can I join anytime?
Yes! Each session covers a standalone project, so you can start anytime and repeat batches if needed.
📍 Will I get code and materials?
Yes - all presentations, source code, and recordings are shared for lifetime access.
📍 Is there live interaction?
Yes - conducted on Zoom with full unmute and Q&A support.
📍 What if I face issues during projects?
You’ll receive WhatsApp support and weekly live doubt sessions for troubleshooting.
📍 What happens if I miss or can’t complete the batch?
You can repeat the next batch for free - no extra cost.