Live audio transcription and other similar applications require stateful processing to support both multi-user sessions and dynamic scale-out. We can persist audio state with a Kafka kappa architecture, but that state must also be preserved across the OpenShift cluster boundary to user web clients. Fortunately, OpenShift's sticky sessions allow stateful sessions to be implemented without complicated custom configurations.
In this talk, Gage will explain how to convert your single user constrained application to support stateful sessions with any number of users. Using the power of OpenShift and Open Data Hub's data monitoring and streaming tools, a stateful architecture can be developed and managed easily. We will showcase a real-time audio transcription use case, including a Kafka streaming architecture, in a practical data science application.
Video Stream