Adaptiv On-Premise Kubernetes-plattform for AI-trening på tvers av flere domener

Offentlig prosjektsammendrag

Organizations with sensitive data cannot use cloud AI platforms due to GDPR requirements, yet on-premise solutions require extensive Kubernetes expertise and achieve only 45-55% GPU utilization. This project develops an adaptive Kubernetes platform that automatically optimizes AI training across diverse data types (genomics, vision, NLP) without manual configuration, achieving cloud-level performance on-premise. R&D Challenges: (1) automatically detecting workload characteristics to optimize resources, (2) creating framework-agnostic pipelines (TensorFlow/PyTorch/JAX) without performance penalty, (3) determining when distributed training helps vs. hurts, (4) optimizing conflicting domain requirements in one system. Method: Iterative experimentation profile workloads, develop hypotheses, build prototypes, systematic A/B testing. Targets: >70% GPU utilization, 70-90% of cloud performance, <2-hour setup. Open-source platform with commercial support.