Sri Harsha Yayi
Staff Product Manager, Intuit
Palo Alto, California, United States
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Sri Harsha Yayi is a Product Manager at Intuit, where he primarily focuses on the company's Modern SaaS Kubernetes platform, specifically within real time event processing domain. He is the PM for Numaflow, an open-source, Kubernetes native platform designed for the development of real time event processing applications at scale
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Numaflow: Powering Scalable Inference on Real-Time Data Streams at Intuit
At Intuit, the ML teams encountered challenges when managing and executing inference on high-throughput streaming data. Integrating with diverse messaging systems like Kafka, Pulsar, and SQS required considerable effort, while enabling intermediate data transformations and inference added complexity. Additionally, dynamically scaling inference workflows to handle fluctuating event volumes posed unique challenges.
To address these issues, we developed Numaflow, an open-source, Kubernetes-native platform designed for scalable event processing. Numaflow simplifies connections to various event sources, empowers teams to process and infer on streaming data with ease, and integrates effortlessly with existing infrastructure. This session is ideal for ML engineers, data scientists, and anyone interested in asynchronous inference on streaming data. We’ll dive into how Numaflow eliminates bottlenecks and optimizes inference on streaming data.
Event Processing on the Edge at scale using Kubernetes native technologies
As organizations generate massive volumes of data from IoT devices, sensors, and other edge systems, the demand for real-time processing at the edge has increased. Edge computing enables low-latency processing, reduces bandwidth usage, and supports time-critical decision-making by moving computation closer to the data source.
This talk introduces Numaflow, Intuit's kubernetes native open-source platform for event processing, and its role in simplifying edge event consumption and processing. Attendees will learn how Numaflow ensures seamless autoscaling to handle dynamic event workloads, guarantees no data loss through its fault-tolerant architecture, and integrates effortlessly within edge environments. The session will also share how the community is leveraging Numaflow for use cases such as IoT data processing and radio signal processing, showcasing its versatility and impact in real-world scenarios
Inference on streaming data at scale at Intuit
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
Sri Harsha Yayi
Staff Product Manager, Intuit
Palo Alto, California, United States
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