Uploaded July 2026 | Updated September 2026, 2 weeks ago
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How Subaru Accelerated AI Model Development for Next-Generation EyeSight with Kubernetes - Ryoji Kobayashi, DevOps Engineer, Subaru Corporation
Subaru is developing AI models in-house to further improve the recognition accuracy of next-generation EyeSight.
As AI development activities expanded, the team faced several challenges, including large ML container images, manual deployment operations, and increasingly complex machine learning workflows.
To address these challenges, Subaru built a Kubernetes-based AI model development platform using cloud native technologies such as Harbor, Envoy Gateway, MetalLB, Argo CD, Helm, and Argo Workflows.
These improvements reduced container image pull time from approximately three hours to three minutes, enabled GitOps management for 25 application definitions, and automated machine learning workflows.
In this session, Subaru will share how it used Kubernetes and CNCF technologies to address key challenges in AI model development and accelerate its development workflow.
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Shanghai, China (8-9 September, 2026) and Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
How Subaru Accelerated AI Model Development for Next-Generation EyeSight with Kubernetes - Ryoji Kobayashi, DevOps Engineer, Subaru Corporation
Subaru is developing AI models in-house to further improve the recognition accuracy of next-generation EyeSight.
As AI development activities expanded, the team faced several challenges, including large ML container images, manual deployment operations, and increasingly complex machine learning workflows.
To address these challenges, Subaru built a Kubernetes-based AI model development platform using cloud native technologies such as Harbor, Envoy Gateway, MetalLB, Argo CD, Helm, and Argo Workflows.
These improvements reduced container image pull time from approximately three hours to three minutes, enabled GitOps management for 25 application definitions, and automated machine learning workflows.
In this session, Subaru will share how it used Kubernetes and CNCF technologies to address key challenges in AI model development and accelerate its development workflow.
