Monitor Ray applications and clusters with Datadog

Bowen Chen and Anjali Thatte introduce Ray, an open-source compute framework that simplifies the scaling of AI and Python workloads for on-premise and cloud clusters. The platform integrates with popular libraries and tools within the machine learning ecosystem, allowing developers to scale complex AI applications without changes to their existing workflows or AI stack. Datadog now integrates with Ray, enabling real-time monitoring and alerting on Ray issues, such as identifying bottlenecks, failing tasks, and optimizing resource efficiency. The integration provides visualizations of telemetry from Ray nodes, alerts for critical issues, and the ability to manage resource consumption and optimize GPU utilization. With this integration, developers can monitor their Ray clusters alongside other AI toolkits and sign up for a 14-day free trial if needed.