Explore how to build a scalable distributed compute cluster for machine learning using Ray on Kubernetes and AWS. Learn from Victor Yap, an MLOps Engineer at Rev.com, as he shares insights on creating a versatile cluster capable of handling diverse compute resources for data processing, model training, and serving. Discover techniques to dynamically scale resources, bridge the gap between local and cluster environments, and efficiently manage any compute problem. Gain valuable knowledge on implementing a unified cluster solution that can adapt to various resource types and quantities, enabling seamless machine learning operations in the cloud.