Jooho lee
Red Hat, Principal Software Engineer
Toronto, Canada
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Jooho Lee is a Toronto-based technology professional with over a decade of experience at Red Hat, working across cloud-native platforms and AI. He began in middleware support and later moved into OpenShift consulting, helping customers design and operate Kubernetes platforms at scale. Today, he is on the OpenShift AI Model Serving team, integrating KServe and LLM-D into Open Data Hub to enable production-grade, Kubernetes-native deployment and operation of AI/ML models. He is also a KServe committer, contributing to the project’s model serving and LLM inference abstractions.
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KServe 101: An Introduction to CNCF’s New Model Serving Project
KServe is a newly accepted CNCF project designed to simplify and standardize machine learning model serving on Kubernetes.
This lightning talk provides a concise introduction to KServe for those new to the project. I will cover what KServe is, what it provides, and its key building blocks—such as InferenceService, serving runtimes and llmisvc(llm-d) —giving attendees a clear mental model of how KServe works and where it fits in modern cloud-native ML platforms.
KServe and the Next Step for LLM Workloads: LLMInferenceService in Context
Traditional ML and LLM serving share the same needs on Kubernetes: consistent deployment APIs, reliable scaling, and an operational model teams can standardize and automate. This session gives an architecture-first overview of KServe, a Kubernetes-native model serving control plane for classic inference and LLM workloads (a CNCF incubating project since Nov 2025). We then explain why KServe introduced LLMInferenceService (LLM-D integration): to support LLM-focused serving patterns within the same KServe foundation, with clear responsibilities and a Kubernetes-native request flow. You’ll leave with a strong mental model of how KServe and LLMInferenceService fit together and how to approach adoption on real platforms. We close with a short demo installing KServe and deploying a minimal inference service (no benchmarking or tuning).
KCD Texas 2026 Sessionize Event Upcoming
KCD Toronto 2026 Sessionize Event Upcoming
CNCF Toronto: 2026 Meetups User group Sessionize Event
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