At Intuit, ML teams faced challenges with processing and running inference on high throughput streaming data. Connecting to various messaging systems like Kafka, Pulsar, and SQS proved to be a time-consuming and intricate process. Moreover, our ML teams required the ability to perform intermediate processing and execute inference as part of their workflows. To further complicate, scaling the processing and inference based on the volume of events introduced additional challenges.

Based on challenges, we created Numaflow, a K8s native open-source platform for scalable event processing. It simplifies connecting to event sources, enables teams to do event processing and inference on streaming data without a learning curve, and integrates seamlessly with existing systems. This talk is for ML engineers, data scientists, and those interested in asynchronous inference on streaming data. We'll show how Numaflow overcomes obstacles and streamlines inference on streaming data