Case Studies | SaaS B2B

Lifting a machine learning product to the cloud

AWS Cloud Migration

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The migration had three focus areas, each addressing a limitation of the existing on-premises environment.

A scalable storage and data foundation

The client's filesystems and databases needed a home that could grow seamlessly with the product while protecting data integrity. On-premises storage offered neither the durability nor the elastic capacity the team wanted, and the database layer needed high availability and automated recovery to underpin everything above it.

Production-grade machine learning workflows

The product's model components had to move from ad hoc execution to repeatable, versioned pipelines, with reproducibility and monitoring built in, so the team could move from experimentation to production without re-engineering the plumbing each time.

A secure, elastic application layer

The backend and frontend needed to auto-scale with demand, authenticate users securely and reduce operational overhead, replacing fixed on-premises capacity with a serverless model and edge protection.

Solution

We delivered the migration under the Cloud Combinator Cloud Accelerator programme, moving from discovery and consultancy through best practices and a hands-on immersion day into project delivery. The technical work was sequenced in phases so each layer was solid before the next was built on it.

6

Migration workstreams delivered end to end

Multi-AZ

Database resilience for high availability

Serverless

Backend on AWS Fargate and Lambda

By the numbers:

  • 6 - Migration workstreams delivered end to end
  • Multi-AZ - Database resilience for high availability
  • Serverless - Backend on AWS Fargate and Lambda
Changes

The migration delivered against the agreed Cloud Accelerator scope: the client's product was re-platformed onto AWS across all six workstreams, and acceptance was confirmed on the scope of delivery, the technical competence of the internal lead, and AWS being demonstrated as a suitable environment to continue developing the product. The projected gains in performance and cost efficiency that motivated the move were built into the target architecture.

  • Scalable storage foundation — Filesystems migrated to Amazon S3 with lifecycle policies, versioning and encryption for durable, cost-aware data management.
  • Production ML workflows — SageMaker Pipelines with asynchronous inference, models in S3 and containers in ECR for reproducibility and controlled deployment.
  • Secure, elastic application layer — A serverless backend on Fargate and Lambda behind API Gateway, with Cognito authentication and IAM access control.
  • Resilient data layer — The relational database moved to Amazon RDS with Multi-AZ deployment, migrated with minimal downtime via AWS Database Migration Service.
  • Handover — A targeted immersion day upskilled the client's team, with sign-off on the internal lead's competence to continue development in AWS.

The client came out of the engagement with an AWS-native, future-proof foundation, no longer constrained by on-premises limits but optimised to scale operations and accelerate model development as the product grows.

AWS Stack

Amazon S3

For durable, scalable storage of files and model artifacts.

Amazon SageMaker Pipelines

For automated, reproducible machine learning workflows.

AWS Fargate

And AWS Lambda for a serverless backend that scales with demand.

Amazon API Gateway

For secure, token-based access to backend services.

Amazon Cognito

For a scalable user directory and authentication.

Amazon RDS

For a high-availability relational database with Multi-AZ deployment.

AWS Amplify

For hosting and CI/CD of the React frontend.

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