Cloud Migration That Cut Deployment Time by 96% and Enabled Daily Releases

Binariks began by understanding the business context behind the migration request.

The key goal of the project was to model loan returnability and profitability – moving mathematical models to the cloud would enable better management of resources, supporting this objective through automated model execution and performance gathering on cloud infrastructure.

Structuring the engagement

The project launched with a clear objective: automate model execution and gather performance metrics on the cloud. Rather than attempting a single large migration, the team adopted Scrumban methodology with two-week sprints.

This allowed for iterative progress – prioritizing which model families moved first, refining approaches based on lessons learned, and maintaining production stability throughout the transition.

Planning and refinement sessions kept the migration aligned with the client’s operational needs. The focus remained on delivering working infrastructure incrementally, not waiting for a perfect final state.

Building for the transition and beyond

The approach centered on three parallel workstreams:

• migrating existing on-premise environments to AWS
• establishing automation that would replace manual provisioning
• and creating comprehensive documentation so the client's team could operate independently post-migration.

Each sprint delivered tangible progress – containerized environments, deployment pipelines, monitoring dashboards – building toward a platform that could scale with demand and deploy with confidence.

Architecture and Infrastructure

We rebuilt the company’s model execution environment as a cloud-native platform on AWS, designed around their specific workload patterns and operational requirements.

Core Infrastructure Components

• Containerized execution environment: Python-based models (Django framework with Pandas for pre-deterministic analytics) packaged as Docker containers, ensuring consistency across development, testing, and production
• Compute orchestration: Amazon ECS (Elastic Container Service) managing containerized applications with automated scaling based on actual workload demands
• Scheduled batch processing: EventBridge Scheduler coordinating model execution timing, allowing optimization for cost and resource availability
• Container registry: Amazon ECR storing versioned Docker images with automated builds from code commits
• Infrastructure as Code: AWS CDK templates defining all infrastructure, organized by department for isolated resource management

Deployment and Automation

The CI/CD pipeline automates the entire deployment flow:

1. Docker image rebuild triggered by code changes
2. Image pushed to ECR with version tagging
3. IAM roles and permissions configured automatically
4. ECS task definitions updated with new image references
5. EventBridge schedules updated or created for batch jobs

Infrastructure provisioning that previously required manual work and hours of waiting now executes in minutes through templated AWS CDK deployments.

Monitoring and Operational Visibility

• AWS CloudWatch provides centralized monitoring of metrics and events across all services
• CloudWatch Logs aggregates logging from all model execution environments, making troubleshooting faster and more effective
• AWS CloudTrail tracks user activity and API calls for security auditing and compliance requirements
• IAM policies enforce least-privilege access controls

Security and Compliance

Identity and access management policies ensure that only authorized users and services can access specific resources. CloudTrail logging provides the audit trail required for regulatory oversight. Encryption protects data at rest and in transit.

Documentation Deliverables

Comprehensive runbooks, dependency documentation, and operational guides were created to support the client's team in managing the new infrastructure. This documentation ensures knowledge continuity and enables confident operations regardless of team changes.