Running inference on streaming data is complex. Teams must connect to event sources, process streams, and scale inference with minimal overhead. Numaflow, a Kubernetes-native open source platform, simplifies this by allowing teams to do stream processing and inference in the same pipelines natively on Kubernetes. It integrates with Kafka, Pulsar, SQS, and more to support diverse event sources and scales to handle high-throughput workloads. Numaflow (created by the creators of Argo Project) is currently in use in by large enterprises.

This talk shows how Numaflow enables ML engineers to achieve scalable stream processing and inference on streaming data without the infrastructure burden.