Modern AI systems demand more than just scalable compute — they require real-time streaming pipelines that are fast, reliable, and adaptable to continuous model updates.
In this talk, learn how a fast-moving AI company transformed its streaming infrastructure from simple durable queues into an intelligent backbone powering both production workloads and research at scale.
Key topics include:
- Scaling Kafka and Flink for high-throughput AI workloads
- Supporting complex AI products like ChatGPT and Sora
- Leveraging abstraction, automation, and observability to maintain resilience during 5× quarterly growth
- Lessons in building streaming infrastructure as the nervous system of modern AI platforms
Whether you’re building AI systems, data infrastructure, or research pipelines, this session provides actionable insights into how streaming drives real-time intelligence and enables rapid innovation.
Aravind Suresh leads the real-time infrastructure team at OpenAI, where he builds large-scale streaming, real-time, and ML infrastructure that powers AI products like ChatGPT and Sora. Previously, he led infrastructure efforts at Uber to enable exabyte scale data analytics and AI initiatives across Rides, Eats, and Groceries. With over seven years of experience, Aravind specializes in designing and operating mission-critical, high-throughput data platforms for real-time analytics and machine learning systems.
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