Chakravarthy Nagarajan

Ray provides a unified Python framework for petabyte-scale data processing, large-model training, and low-latency inference. In this hands-on workshop, you will use Ray on SageMaker HyperPod to a model and set up endpoints.

Frontier models are powerful but costly to deploy. On-policy distillation, adopted by Qwen3, MiMo, and GLM-5, lets you compress a large teacher into a lean, domain-specialized student by training on the student's own rollouts with dense token-level feedback.

Open-weight models deliver exceptional performance while offering customization control. Organizations can process sensitive data locally, deploy models tailored to specific requirements, and scale efficiently at lower latency and cost.