Hajnal Máté
Senior MLOps Engineer, CNCF-Volcano Core Contributor, LFX Mentor, Kubeastronaut
Kecskemét, Hungary
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Mate Hajnal is a Senior Machine Learning Operations Engineer at Aumovio, following the spin-off of Continental’s automotive branch. With a background in DevOps and Platform Engineering at Red Hat and Nokia, he brings deep expertise in cloud-native infrastructure, Kubernetes, and scalable ML systems. Mate is an active contributor to the CNCF ecosystem and enjoys building bridges between data science, artificial intelligence and modern operational practices.
Area of Expertise
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Volcano: Orchestrating the Full AI Lifecycle – From Training to Inference and Agents
The rapid evolution of AI has led to infrastructure fragmentation, where training, inference, and agent workloads run in isolated systems, causing resource inefficiency. Volcano addresses this as a Unified Scheduling Platform for the full AI lifecycle, delivering robust scheduling capabilities with high throughput.
Volcano is evolving into the next-generation platform capable of orchestrating diverse workloads beyond batch jobs, enabling multi-scheduler coordination.
At the workload layer:
- Volcano-Global splits massive training jobs across clusters, removing single-cluster limits
- Kthena delivers enterprise-grade LLM serving with frameworks like vLLM
- AgentCube enables rapid agent workload scheduling
At the infra layer, Volcano provides modern resource abstraction through DRA integration, HyperNode discovery, GPU sharing, and heterogeneous pooling for efficient task-to-accelerator mapping.
Join us to explore how Volcano is shaping the future of Cloud Native AI infra.
More Than YARN on K8s: Unified Queuing for Mixed AI & Big Data Workloads
The greatest challenge in migrating YARN workloads to Kubernetes is perfectly replicating its robust multi-tenant capabilities. This session demonstrates how Volcano uses hierarchical queues as the cornerstone for multi-tenancy, providing granular quota management, fair-share scheduling, and powerful cross-queue elastic preemption.
But the true evolution lies in moving beyond YARN. We will then show how Volcano provides an advanced framework for heterogeneous hardware under a single, unified queuing system. We will dive deep into the scheduling capabilities that the YARN model lacks but modern AI platforms demand, including: tenant-to-hardware affinity, workload-aware resource isolation, and performance-driven topology awareness (for networks, GPUs, etc.).
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