Architecture
Choose AWS services and deployment patterns around the workload’s real availability, scalability and security requirements.
AWS Case Study · AI / Software
Serverless containers and CI/CD for an AI application.
The Bold Group needed a scalable and cost-efficient deployment model for an AI application, together with a release process that allowed developers to ship updates without manually managing servers or deployment steps.
The challenge
What had to change.
The application needed to scale during demand peaks, remain highly available and support continuous integration and delivery while reducing infrastructure management overhead.
Our solution
How Ghaim approached it.
Ghaim containerized the application and built an AWS deployment platform around ECS Fargate, load balancing, Auto Scaling, CodePipeline, CodeCommit and CloudFormation.
- Containerized application components for portable and consistent deployments.
- Deployed containers using Amazon ECS with AWS Fargate to remove server capacity management.
- Added Application Load Balancer and Auto Scaling across Availability Zones.
- Automated releases with AWS CodePipeline and source control in AWS CodeCommit.
- Used CloudFormation for repeatable Infrastructure as Code deployment.
FargateServerless container compute
Auto ScalingDemand-based capacity
CI/CDRepeatable automated releases
AWS services used
What this demonstrates
AWS architecture tied to operating outcomes.
Each case study reflects the same Ghaim approach: secure foundations, automation, resilience, cost awareness and a clear operating model after deployment.
Automation
Use Infrastructure as Code and CI/CD to reduce manual operational risk and make changes repeatable.
Operations
Design monitoring, recovery and ongoing optimization into the platform instead of adding them after go-live.
Related expertise
Explore the capabilities behind this work.
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