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CAnDL Platform

·2 mins

Project Overview #

Developed a container-based platform for deploying analytics and AI applications for internal teams, focusing on security compliance and developer productivity.

Technical Architecture #

Infrastructure Foundation

  • AWS ECS Fargate for containerized application runtime
  • Terraform modules for reproducible infrastructure deployment
  • Multi-environment support (development, testing, production)
  • Integration with organizational identity management systems

Security Implementation

  • Migration from standard Debian images to Chainguard minimal containers
  • Achieved zero-CVE baseline across all production containers
  • Implemented security scanning integration within CI/CD pipelines
  • Network isolation through VPC endpoint configurations

New AI/ML Integration

  • Amazon Bedrock integration with custom guardrails
  • RAG architecture using OpenSearch vector storage
  • Support for multiple foundation models with dynamic selection
  • Token usage monitoring and cost optimization

Development Methodology #

Infrastructure as Code

# Example Terraform module structure
module "multi_user_app" {
  source = "./modules/ecs-app"
  
  app_name         = var.app_name
  user_config      = local.user_configurations
  container_image  = "${var.ecr_repository}:${var.image_version}"
  
  # Security configurations
  vpc_config       = var.vpc_settings
  security_groups  = var.security_group_ids
  
  # Monitoring integration
  cloudwatch_config = var.monitoring_settings
}

CI/CD Automation

  • Automated version tagging using conventional commit standards
  • Container image building and registry publishing
  • Terraform validation and security scanning
  • Integration testing with containerized dependencies

Collaborative Aspects #

This project involved close coordination with:

  • Data science teams for application requirements
  • Security architects for compliance validation
  • Infrastructure specialists for networking design
  • Project delivery teams for deployment scheduling

Technical Outcomes #

  • Deployment frequency improved from monthly to weekly cycles
  • Container security vulnerabilities reduced to zero
  • Developer onboarding time decreased through standardized tooling
  • Infrastructure consistency achieved across multiple environments

Learning Points #

Working within government compliance requirements provided valuable insights into balancing security constraints with developer productivity. The experience highlighted the importance of early security integration rather than retrofitting compliance measures.

Technology Stack: AWS ECS, Terraform, Docker, Python, GitLab CI/CD

Loïc Houpert
Author
Loïc Houpert
I’m a cloud engineer at Development Seed focused on geospatial data infrastructure and Earth observation systems. I build cloud-native pipelines that turn massive satellite archives into analysis-ready datasets for organizations like ESA and EUMETSAT. Working daily with Kubernetes, Argo Workflows, and cloud-native geospatial formats like Zarr and STAC, I combine a decade of scientific data processing experience with modern cloud engineering practices.