Oscar Rovira

ml-inference spot-instances gpu-fractionalization gpu cost-optimization generative-ai llm cloud-computing aws gcp azure mlops

Dmitry Petrov

dataset-sharing annotations generative-ai ml data-versioning machine-learning dvc cost-optimization collaboration embeddings

Yubo Gao

visual-profiler training-throughput batch-size mixed-precision tensor-cores flash-attention deep-learning machine-learning performance-optimization profiling gpu-utilization pytorch tensorflow model-deployment cost-optimization energy-efficiency

Guy Eshet

llm ai ai-infrastructure llm-ops prompt-engineering model-deployment gpu data-pipelines rag cost-optimization generative-ai llm-applications

Arnaud Meukam Davanum Srinivas

kubernetes kubernetes-infra sig cloud-native infrastructure aws gcp cncf cost-optimization ci-cd supply-chain registry

Aparna Subramanian Todd Ekenstam Phillip Wittrock Nagarajan Chinnakaveti Thulasiraman

cloud-computing platform-efficiency cost-optimization kubernetes autoscaling finops cloud-costs design culture observability ci-cd

David Hadas Michael Maximilien

knative kubernetes serverless microservices cloud-native automation auto-scaling security cost-optimization developer-experience

Cheryl Hung

multi-arch infrastructure kubernetes arm x86 ci-cd cloud-native aws-graviton containers devops performance cost-optimization

Irvin Lim Hailin Xiang

kubernetes hadoop yarn big-data cost-optimization resource-management cgroups kernel container-runtimes scheduler kubelet data-infrastructure

Cindy Blake Ido Neeman

sbom ibom cloud-native infrastructure-management security supply-chain-security asset-management kubernetes compliance cost-optimization attack-surface-management drift-detection