<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Case-Study on CuraDevOps</title><link>https://curadevops.metacog.co.kr/tags/case-study/</link><description>Recent content in Case-Study on CuraDevOps</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 29 Jul 2026 12:56:53 +0000</lastBuildDate><atom:link href="https://curadevops.metacog.co.kr/tags/case-study/index.xml" rel="self" type="application/rss+xml"/><item><title>Subaru CNCF Case Study: 60x Faster AI Container Image Pulls</title><link>https://curadevops.metacog.co.kr/insights/2026-07-29-subaru-wins-cncf-end-user-case-study-contest-for-acceleratin/</link><pubDate>Wed, 29 Jul 2026 12:56:53 +0000</pubDate><guid>https://curadevops.metacog.co.kr/insights/2026-07-29-subaru-wins-cncf-end-user-case-study-contest-for-acceleratin/</guid><description>&lt;ul>
&lt;li>&lt;strong>Platform/SRE — Learn:&lt;/strong> Illustrates real-world gains from optimizing container image pull pipelines for AI workloads; no operational change required, but worth reviewing the architecture patterns if you run similar GPU/AI workloads on Kubernetes.&lt;/li>
&lt;li>&lt;strong>CI/CD — Skip&lt;/strong>&lt;/li>
&lt;li>&lt;strong>Leader — Learn:&lt;/strong> A concrete benchmark (60x image pull improvement) from a major manufacturer adopting cloud-native for AI/ADAS development; useful context for internal platform investment conversations, but no decision is forced.&lt;/li>
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