Running and Monitoring Distributed ML with Ray and whylogs

Running and monitoring distributed ML systems can be challenging due to the need to manage multiple servers and different logs. However, Ray simplifies parallelizing Python processes, while whylogs enables users to monitor ML models in production even in a distributed environment. The key advantage of whylogs is its ability to operate on mergeable profiles that can be easily generated in distributed systems and collected into a single profile for analysis. This post explores options for integrating whylogs into Ray architectures as a monitoring solution.