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Jesse Butler

Principal product manager at Amazon Web Services (EKS team) with a background in operating systems and containers, working on GPU enablement in Kubernetes and cloud-native standards and conformance efforts.

Top 3 podcasts with Jesse Butler

Ranked by the Snipd community
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23 snips
Apr 1, 2025 • 44min

Kubernetes Resource Orchestrator (KRO), with Jesse Butler and Nic Slattery

Jesse Butler, a Principal Product Manager at AWS, and Nic Slattery, a Product Manager at Google, dive into the Kubernetes Resource Orchestrator (KRO). They discuss KRO's mission to simplify resource management for developers and platform admins, emphasizing its user-friendly approach and integration with Custom Resource Definitions. The conversation touches on the challenges of navigating the Kubernetes ecosystem and the role of open-source collaboration in enhancing orchestration. They also compare KRO with traditional tools, showcasing its flexibility and efficiency.
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5 snips
Apr 15, 2025 • 22min

The Kro Project: Giving Kubernetes Users What They Want

Guests Jesse Butler, Principal Product Manager at AWS, and Nic Slattery, Product Manager at Google Cloud, discuss the exciting new Kro project born from customer demand. This open-source tool aims to simplify Kubernetes resource orchestration across cloud providers, reducing complexity for developers. They share insights on the unique collaboration among Google, Amazon, and Microsoft, driven by community needs rather than top-down decisions. The project has quickly gained traction, attracting numerous contributors as it prepares for a production-ready release.
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Dec 11, 2025 • 35min

Kubernetes GPU Management Just Got a Major Upgrade

Kevin Klues, a distinguished engineer at NVIDIA, and Jesse Butler, a principal product manager at AWS, explore cutting-edge advancements in Kubernetes for AI. They discuss the transformative Dynamic Resource Allocation (DRA), which allows users to specify GPU types and configurations, enhancing GPU usability. Klues highlights an upcoming workload abstraction aimed at optimizing complex AI workloads, ensuring coordinated multi-node jobs. They emphasize community involvement in shaping Kubernetes’ AI capabilities and the importance of balancing efficiency with system complexity.

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