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Featured Assignments
End-to-End CI/CD Pipeline Using GitHub Actions and Docker for a Node.js App
DevOps Automation & CI/CDClient Requirements
The student wanted to design and implement a fully automated CI/CD pipeline for a Node.js-based microservice. They specifically mentioned needing a solution that uses GitHub Actions, with stages for building Docker containers, running tests, pushing to Docker Hub, and deploying to a test server.
Challenges Faced
We ensured pipeline modularity, but we faced some complications while dealing with Docker caching issues and securing credentials for Docker Hub integration through GitHub secrets. Ensuring consistent behavior across development and deployment environments also required fine-tuning the Dockerfile.
Our Solution
We set up a .github/workflows YAML pipeline that ran on every push to the main branch. The pipeline included steps for linting, testing, building the Docker image, tagging it, pushing it to Docker Hub, and finally deploying it to a remote server using SSH. All secrets were encrypted and environment variables managed securely.
Results Achieved
The student ended up with a fully operational CI/CD workflow that reduced manual intervention and accelerated safe deployment cycles — all version-controlled and reproducible.
Client Review
Assignment D.O.101 was an incredible experience. I saw my code go from commit to live deployment without lifting a finger. The whole pipeline ran cleanly, the Docker build was smooth, and everything was properly set up. This assignment really helped me understand real DevOps automation.
Infrastructure as Code with Terraform – AWS VPC, EC2, and S3 Setup
Infrastructure as Code with TerraformClient Requirements
The student wanted to learn how to use Terraform to provision infrastructure on AWS, particularly aiming to create a custom VPC setup with public/private subnets, EC2 instances, and an S3 bucket — all defined in code and reusable.
Challenges Faced
We ensured the Terraform modules were cleanly structured, but faced some complications handling state file locking, IAM permissions, and ensuring the VPC was correctly peered with subnets for internet access. Managing dependencies between modules and resources also added complexity.
Our Solution
We wrote a multi-file Terraform configuration that used variables, outputs, and modules to build an AWS VPC, two EC2 instances (public/private), route tables, internet gateway, NAT, and an S3 bucket. Terraform Cloud was used for state management and versioning.
Results Achieved
The student received a complete, reproducible cloud infrastructure that could be spun up or destroyed in one command. Everything was scalable and production-ready in principle.
Client Review
Assignment D.O.202 was next-level! The way the infrastructure came alive from code felt powerful. The VPC, S3, and EC2 were all neatly configured and explained. Working with them gave me the kind of real cloud provisioning clarity I never got in tutorials.
Kubernetes Deployment of a Multi-Container Microservice App
Kubernetes & Cloud-Native DeploymentClient Requirements
The student aimed to simulate real-world deployment by containerizing and orchestrating a multi-container microservice app using Kubernetes (K8s). The task included writing YAML files for pods, deployments, services, and ingress, along with monitoring via Prometheus.
Challenges Faced
We ensured the cluster setup and microservices were modular, but faced challenges managing service discovery between pods, handling volume mounting for persistent data, and setting up Prometheus scraping configs in custom namespaces.
Our Solution
We containerized a frontend, backend, and database service with Docker, then defined Kubernetes manifests for each deployment, service, config map, and ingress. Prometheus and Grafana were added using Helm charts for real-time monitoring. We deployed the entire stack on Minikube and exported all configurations.
Results Achieved
A fully functional K8s microservice ecosystem with metrics dashboards and a stable internal network — all deployable with a single kubectl apply.
Client Review
Assignment D.O.303 was beyond what I imagined. The K8s manifests were precise, the services worked together like a charm, and the monitoring setup with Grafana looked so professional. Everything was orchestrated properly, and I gained confidence like never before.
DevOps Monitoring & Alerting System Using Prometheus, Grafana, and Alertmanager
DevOps Monitoring & ObservabilityClient Requirements
The student wanted to build a monitoring solution that could track server health, application performance, and trigger alerts via email using Prometheus and Grafana, with a proper dashboard and Alertmanager integration.
Challenges Faced
We ensured metrics were detailed and accessible, but faced complications with Prometheus scrape targets, creating meaningful custom alerts, and getting SMTP email setup through Alertmanager to work with Gmail’s security policies.
Our Solution
We deployed Prometheus to scrape data from custom exporters (Node Exporter, app metrics), visualized them using Grafana dashboards with thresholds, and configured Alertmanager to send alerts based on CPU, memory, and disk usage. YAML configurations for alert rules and SMTP settings were carefully tested.
Results Achieved
A production-grade monitoring stack with real-time graphs and working alert notifications. The student learned the operational side of keeping services healthy and predictable.
Client Review
D.O.404 blew my mind. The dashboards were crystal clear, alerts were instant, and the whole setup felt enterprise-level. I finally understood what it means to be proactive in DevOps. The work was done so neatly, I’d 100% work with them again!