Case Study: Enabling CGI to Manage Kubernetes Securely Across Multiple Clouds

Katy Ryder, December 17, 2021


CGI’s Emerging Technology Practice (ETP) had been developing a machine learning platform with monitoring and logging capabilities, called Lovelace, to simplify the deployment of machine learning workloads and enhance the overall developer experience for their customers. 

The small internal team of Machine Learning Engineers was developing on an AWS EKS cluster created with self-managed AWS CloudFormation templates, which could not be used to provision other cloud-managed Kubernetes clusters (i.e. on Azure Kubernetes Service, AKS, or Google Kubernetes Engine, GKE).

The problem

Being limited to one cloud is a huge limitation for their customers, and they needed a secure, scalable and cost-efficient solution to support the deployment of their platform and workloads across all major public cloud providers

If they were to build this capability in-house, there would be a significant overhead in engineering and time to alter their current implementation to a multi-cloud deployment strategy – translating their CloudFormation templates which is cloud provider-specific service, across each major cloud provider, while also taking them away from other critical activities.

The solution

As a trusted partner, CGI called on Appvia’s cloud-native expertise for support and guidance to provide a cost-effective and scalable solution. 

Wayfinder reduced the lead time of deploying secure applications across multiple clouds from 1 week to 1 hour, and enabled developers to continue building applications, with the peace of mind that their Kubernetes clusters are well-managed and adhere to security best practices.

“It was very productive to see the decrease in the required effort to have Kubernetes clusters up and running in production-ready scenarios”João Dinis, Lead Machine Learning Engineer , CGI

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