Grammarly's diverse teams required reliable access to computing resources, prompting a redesign of their ML infrastructure due to limitations in their legacy system. The old system struggled with scalability, support, and security, leading to inefficiencies and resource wastage. The new infrastructure, built using open-source technologies like Kubernetes and Karpenter, improved resource allocation, reduced setup time, and enhanced security. The transition faced adoption challenges as users adapted to new workflows. Ultimately, the upgrade yielded measurable benefits, including faster resource access and improved collaboration across teams. Insights gained included the importance of addressing user needs early and maintaining flexibility in tooling decisions.
https://www.grammarly.com/blog/engineering/ml-infrastructure-research-experimentation/