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World Congress 2026 Europe • Jul 10, 2026 • Session details

Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬

Jeremy Murray

Infrastructure bottlenecks and Slurm queues cripple medical AI. Treating Kubernetes as a global control plane enables secure, hybrid GPU bursting without moving governed patient data to the public cloud.

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#1 about 2 min

Empowering life science researchers with artificial intelligence

Making advanced capabilities more accessible empowers researchers to find cures for complex diseases.

#2 about 3 min

Infrastructure barriers and compliance risks in research

How reliance on local laptops and outdated job schedulers limits scalability and compromises data compliance.

#3 about 2 min

Transitioning to hybrid clouds amid GPU scarcity

Why research institutions are adopting hybrid models to combat vendor lock-in and severe computing shortages.

#4 about 2 min

Understanding data sovereignty and international compliance

Why strict data privacy regulations like the Cloud Act warrant the strategic use of neo-clouds.

#5 about 4 min

Utilizing artificial intelligence to analyze neurodegenerative diseases

Discovering patterns in aging and conditions like Parkinson's and sepsis through advanced data analytics.

#6 about 2 min

Managing complex heterogeneous data in life sciences

Why traditional flat files fail to support the growing petabytes of unstructured medical and multi-omics information.

#7 about 3 min

Bursting GPU capacity over hybrid Kubernetes networks

Using a robust networking fabric to instantly bridge on-premises data with remote cloud compute resources.

#8 about 3 min

Providing researchers with on-demand analytics environments

Deploying self-serve databases, standardized pipelines, and serverless architectures to accelerate scientific model development.

#9 about 2 min

Managing GPU quotas and multi-tenancy with Kueue

Replacing traditional schedulers with a modern queue system ensures fair resource distribution among diverse teams.

#10 about 2 min

Abstracting infrastructure complexity with automated toolsets

Utilizing specialized assistants to help professionals effortlessly deploy suitable models and diverse database architectures.

#11 about 2 min

Scaling model inference leveraging serverless edge functions

Decoupling generalized computations into serverless APIs allows disparate ecosystems to utilize accessible AI endpoints.

#12 about 2 min

Reviewing key architectural decisions for federated AI computing

Summarizing how orchestrated platforms securely bring scalable compute to sensitive data across varied scientific applications.

Matching moments

4:49 min

Structuring compute and data services for AI models

Radu Vunvulea Radu Vunvulea · WWC 2025

3:28 min

Challenges of shared Kubernetes clusters for AI workloads

Piotr Zaniewski Piotr Zaniewski · WWC Europe 2026

1:37 min

Unlocking direct GPU access within managed Kubernetes platforms

Kevin Klues Kevin Klues

3:17 min

Maximizing cloud native capabilities for scaling dynamic AI workloads

Jim Clark Jim Clark +3 · WWC 2025

3:35 min

Serving production machine learning workloads with KubeFlow and KServe

Aarno Aukia · LIVE

2:19 min

Cloud infrastructure deployment and industry use cases

Mingshen Sun Mingshen Sun · WWC 2024

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