Leading financial services firm accelerates ML model deployment from months to days with SageMaker MLOps platform

A leading financial services company struggled with a fragmented ML infrastructure where models took 2-3 months to move from development to production. Data scientists spent over 60% of their time on infrastructure tasks rather than model development. The existing DataRobot platform was becoming costly to scale, and there was a lack of proper model governance and audit trails required for financial industry compliance.

Melio AI implemented a comprehensive end-to-end MLOps platform on AWS SageMaker, including SageMaker Pipelines for CI/CD, SageMaker Canvas for low-code model development, and a custom DataRobot migration path via Docker containers. The platform included a secure VPC design, IAM role-based access control, Infrastructure as Code via Terraform and CloudFormation, and a model registry with automated approval workflows.

The platform standardised ML workflows and enabled rapid progression from experimentation to production deployment. Enterprise security and compliance requirements were met through multi-layered VPC isolation, IAM controls, and complete audit trails via CloudTrail and CloudWatch. Business analysts gained self-service model development capabilities via SageMaker Canvas, reducing dependency on technical teams.

• Migrating from a proprietary ML platform (DataRobot) to a cloud-native MLOps platform (SageMaker) can significantly reduce both cost and deployment cycle time at scale.
• Standardised templates and CI/CD pipelines are essential for reducing the infrastructure burden on data scientists and accelerating time-to-production.
• Self-service tooling (e.g. SageMaker Canvas) can democratise ML development beyond technical teams, increasing organisational ML capacity.