Comprehensive technical analysis of the RAY framework, exploring its architecture, components, and mechanisms that enable scalable distributed computing for machine learning workloads. It identifies key challenges inherent in scaling RAY to very large clusters, such as reliability, resource management, scheduling, and observability issues. The sources then detail RAY's technical innovations and solutions designed to address these challenges, including fault tolerance, autoscaling, advanced scheduling policies, and memory management techniques.
Finally, the text discusses the implications and potential use cases of scaling RAY to handle complex, high-volume workloads, positioning it within the broader landscape by comparing it to Apache Spark and Dask.
