Public Sector Financial Institution Modernizes MLOps with AWS SageMaker
At a glance
A Canadian public-sector financial institution partnered with New Math Data to modernize and unify its machine-learning operations (MLOps). Although the organization had mature data-science teams and a strong AWS foundation, inconsistent tools and workflows slowed delivery and increased risk. New Math Data implemented a secure, standardized MLOps platform powered by Amazon SageMaker, enabling faster development, stronger governance, and a scalable base for future AI innovation.
Industry
Use Case
Standardized MLOps platform for secure, scalable model development and operational use
Solution implemented
- Deployed Amazon SageMaker in VPC only mode to meet isolation and compliance requirements
- Integrated AWS CodeCommit for secure version control and collaboration
- Created custom container images for offline dependency management
- Automated infrastructure provisioning using AWS CDK for consistency and repeatability
- Delivered onboarding templates and prescriptive examples to accelerate adoption
The value equation
- Faster, safer ML model development and deployment across teams
- Reduced operational risk through standardized governance
- Lower development and infrastructure costs via shared workflows
- Improved organizational maturity in MLOps practices
- A scalable foundation for future AI and ML innovation
Company Snapshot
A Canadian public sector financial institution providing lending and financial services across multiple government programs.
Location
Canada
Building a Unified, Secure MLOps Framework on AWS
New Math Data designed and implemented a centralized MLOps platform that standardized the entire model development lifecycle, from experimentation to production, reducing redundancy and operational risk while enforcing consistent best practices across the organization.
Problem
Disparate ML workflows and a lack of standardization slowed delivery and increased operational risk. Each data-science team operated in its own silo, choosing tools, writing code, and deploying models independently. This created redundant infrastructure, inconsistent governance, longer development cycles, higher costs, and limited visibility into ML performance.
They needed a unified, scalable platform to:
- Centralize and standardize model development workflows
- Meet strict privacy and security requirements
- Reduce time-to-deployment and overhead
- Onboard teams quickly while enforcing best practices
Solution
Standardized MLOps Platform with AWS Native Services
New Math Data delivered a centralized MLOps platform built on Amazon SageMaker, deployed in VPC only mode to meet isolation and security mandates.
AWS CodeCommit provided version control and collaboration, while prescriptive templates standardized development practices. Custom container images supported offline package requirements, and the platform was deployed using AWS CDK for secure, repeatable provisioning.
Production-Ready Deployment
The platform operated within a dedicated AWS account to ensure data isolation. SageMaker ran in VPC only mode to maintain compliance, and CodeCommit integration established a single, auditable workflow. Prescriptive onboarding materials, including reusable templates and environment configurations, eliminated one-off setups and promoted best practices.
Over a focused engagement, New Math Data delivered a secure, scalable, and production-ready MLOps platform that aligned with governance requirements and accelerated ML velocity across teams.
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