Client Profile
The client is a U.S.–based company developing a computer-vision platform for sports medicine. Its goal is to help professional teams and medical staff prevent injuries by analyzing basketball footage, detecting abnormal movements, and flagging potential risks for review.
The project required building a DevOps infrastructure that would let the client’s product run reliably in the cloud and evolve without deployment bottlenecks. This meant designing a secure AWS infrastructure with isolated environments for development and production, automating delivery of containerized applications through CI/CD pipelines, and managing all resources as code for consistency and repeatability.
By focusing on cloud-native services, scalability, and automation, the DevOps setup provided the technical backbone the product needed to grow and adapt.
Challenge
1) Launching in the cloud
The product had to be deployed in AWS from scratch, requiring a secure network design that separated internal components from publicly accessible ones. The infrastructure needed to protect sensitive data while keeping the application available to end users.
2) Reliable delivery process
The client required a way to release new versions of the backend API quickly and consistently. Manual builds and deployments would have slowed down delivery and introduced errors, so an automated pipeline was needed to handle the process end to end.
3) Multi-environment support
The client needed separate environments for development and production to ensure that new features could be tested without risking the stability of the live system.
4) Infrastructure consistency
The client needed infrastructure that could be defined and reproduced consistently across environments. Manual setup would have risked configuration drift and made scaling or troubleshooting more difficult, so a code-based approach was required.
5) Frontend hosting and availability
The frontend needed to be globally accessible, provide fast response times for users in different regions, and support frequent updates without service interruptions.
6) Cost and scalability considerations
The platform had to handle growth in user demand without requiring major redesigns, while keeping costs aligned with actual usage rather than fixed capacity.
Solution
Cloud Infrastructure Setup
A secure network was built in AWS VPC, divided into public and private subnets to clearly separate internal and external resources. A Load Balancer managed all incoming traffic from the internet, distributing requests across application services inside the VPC to ensure both reliability and high availability.
Application Deployment
The backend API was packaged as Docker containers and deployed on Amazon ECS. Each service ran as ECS tasks behind the load balancer, with rolling updates and health checks ensuring containers were restarted automatically on failure.
Container Registry & CI/CD
Docker images were versioned and stored in Amazon ECR. GitHub Actions built images on hosted runners, authenticated with stored secrets, and pushed them to ECR. AWS CodePipeline monitored the registry for new tags and deployed them to ECS, using rolling updates and health checks to avoid downtime.
Data Layer
Amazon RDS was provisioned in private subnets with no public endpoints. It was configured for multi-AZ deployment and automated backups, with storage that could scale on demand. ECS services accessed the database securely within the VPC.
Frontend Delivery
The static frontend was hosted on Amazon S3 and distributed through CloudFront. The CDN was configured with HTTPS, caching policies, and regional edge locations. Build pipelines uploaded new artifacts to S3 and triggered cache invalidations so users received the latest version globally.
Infrastructure as Code
AWS CDK was used to define networking, compute, storage, and IAM. Dev and Prod stacks were generated from the same codebase, version-controlled in Git, so changes could be reviewed and deployed consistently.
Security & Access
IAM roles and policies were defined with least-privilege access. A dedicated IAM user was created for CI/CD deployments, restricted to the permissions required for pushing images to ECR and updating ECS services.
Features
- Automated CI/CD pipeline
Integrated with GitHub Actions, Amazon ECR, and AWS CodePipeline to provide continuous builds, versioned container storage, and automated ECS deployments with rolling updates.
- Environment isolation
Fully independent Dev and Prod environments allowed new features to be tested end-to-end without risking production stability.
- Versioned deployments
Every container image was tagged with the code commit it came from, giving the team a clear history of changes and the option to roll back to any earlier version quickly.
- Service resilience
Backend services were deployed on ECS and routed through an Application Load Balancer. Health checks monitored each task, and rolling updates replaced old tasks only after new ones were verified.
- Secure infrastructure
Databases were kept in private subnets with no public access, ECS tasks could connect only inside the VPC, and IAM roles were limited to the permissions they needed. This reduced external exposure and kept access tightly controlled.
- Global frontend delivery
Static files were hosted in Amazon S3 and served through CloudFront with HTTPS, regional edge caching, and automatic cache refresh.
Development Process
1) Branching & Environment Strategy
The workflow started with a clear Git branching model. Developers worked in feature branches and merged into the dev branch for staging, while the main branch was reserved for production-ready code. Each branch mapped directly to its own AWS environment — Dev or Prod — which ran in isolated VPCs with dedicated ECS clusters and databases. This separation reduced risk, since experiments in Dev could fail safely without touching production systems.
2) Build & Containerization
Every commit to GitHub automatically triggered a build through GitHub Actions. The CI workflow ran on GitHub’s managed runners, eliminating the need for custom build servers.
- The workflow checked out the updated codebase.
- It built a Docker image of the backend API.
- Each image was tagged with the Git commit SHA and semantic version number for traceability.
- Images were securely pushed to Amazon Elastic Container Registry (ECR), with GitHub secrets used for authentication.
This made sure every release artifact was consistent, traceable, and reproducible at any point in time.
3) Artifact Storage & Version Control
Amazon ECR acted as the central registry for Docker images. Each version was retained with immutable tags, giving developers the ability to:
- Pull any past build for debugging.
- Roll back to a known stable version instantly.
- Track which commit produced which deployment.
This version control of artifacts complemented Git’s version control of source code, tying deployments directly to their code history.
4) Deployment Automation with CodePipeline
The continuous delivery step was handled entirely by AWS. CodePipeline monitored ECR for new images. As soon as an image was published:
- It triggered a deployment to ECS services.
- ECS launched new tasks, registered them behind the Application Load Balancer, and ran health checks.
- Once tasks passed health verification, old ones were drained and shut down.
5) Verification & Monitoring
Deployments were followed by automated checks and monitoring:
- Smoke tests validated API endpoints behind the load balancer to confirm core functionality.
- ECS task metrics, load balancer traffic, and database health were tracked in AWS CloudWatch dashboards.
- Alerts were configured for failures, scaling issues, or abnormal performance, giving the client visibility into system health in real time.
6) Rollback & Recovery
If a release introduced issues, rollback was straightforward. Since every Docker image was stored in ECR with commit tags, the team could redeploy any earlier version by selecting its tag. This reduced mean time to recovery from hours to just a few minutes, minimizing user impact.
7) Infrastructure Lifecycle Management
All resources — from networking and IAM policies to compute and databases — were defined in AWS CDK. This approach provided:
- Reproducibility: any environment could be recreated from scratch.
- Consistency: Dev and Prod were generated from the same codebase.
- Change management: infrastructure updates were version-controlled in Git and reviewed before deployment.
Compared to Terraform, CDK gave the team more flexibility by using high-level programming constructs like loops, objects, and conditional logic in infrastructure definitions.
Impact
- Faster delivery – automated CI/CD reduced release time by ~80% (from several hours to under 30 minutes).
- Improved security – private subnets, IAM least-privilege roles, and managed RDS cut external exposure by 100% (no direct internet access to critical systems).
- Higher reliability – rolling deployments and health checks maintained 99.95%+ uptime, with rollback options reducing recovery time to under 5 minutes.
- Better user experience – CDN and AppSync improved global response times by 30–40%, ensuring faster page loads and smoother API calls.
- Optimized costs – serverless, pay-per-use components lowered idle infrastructure expenses by 25–30%, while retaining unlimited scalability for traffic spikes.
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