Mayank Juneja
Product Manager, Stream Processing at Confluent
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Mayank is a Product Manager for Stream Processing at Confluent, leading real time AI initiatives. He holds extensive experience of building and launching enterprise software products, with stints in VMware, Amazon, and Bidgely, an AI clean-tech startup.
Mayank holds an MBA with a specialization in Artificial Intelligence from Northwestern University, and a Computer Science degree from BITS Pilani, India.
The Future of Agentic AI is Event-Driven: How to Build Streaming Agents on Apache Flink
At their core, AI agents are microservices with a brain. They're powered by large language models (LLMs) and are independent, specialized, and designed to operate autonomously.
But agents need more than LLMs to scale -- they need real-time data access, context, and the ability to collaborate across tools, services, and even other agents. As timely data becomes crucial for modern AI systems, agents must operate within distributed, event-driven environments. This session focuses on how to bridge the gap between streaming infrastructure and agentic architectures, by enabling developers to build, test, and operate agents natively on Flink.
Through architecture diagrams, use cases and a demo, we'll show practical steps for getting started with streaming agents to power new automation workflows.
Data-centric AI with Flink SQL
Generative AI is revolutionizing the technology stack, introducing a new era of innovation and automation. Apache Flink is keeping up with this revolution through FLIP-437, which introduces AI models as first-class citizens in Flink SQL, on par with Tables, seamlessly integrating them into the existing resource hierarchy (Catalog->Database->Model). This groundbreaking advancement paves the way for unified data processing and model inference using the familiar SQL interface and positions Flink as a central piece of modern AI architectures.
In this 30-minute session, tailored for developers with experience in stream processing, we'll start by exploring the rationale behind Flink's decision to treat AI models as first-class entities within Flink SQL. We’ll then delve into the newly introduced ML_PREDICT and ML_EVALUATE functions, which enable a generic way for model inference and evaluation using Flink SQL. Finally, we’ll demonstrate how to leverage the powerful combination of Flink and Kafka to create an anomaly detection app that harnesses real-time data streams.
By the end of the session, attendees will understand how they can write an end-to-end data app with seamless AI inference integration using **just** Flink SQL.
Why Serverless Flink Matters - Blazing fast stream processing made scalable
The shift from batch processing to real-time processing of data is accelerating. Building real-time data applications is a necessity for many businesses as customers expect data to be always up-to-date and their apps to react to changes as they happen. However building and productizing real-time applications is often a complex and lengthy process due to limited serverless options to build such apps.
The introduction of AWS lambdas was a watershed moment in the world of cloud computing. It allowed developers to fire up “fully-managed” computer programs while paying for only when the program ran. Serverless compute comes with three big advantages - improved scalability, reduced cost, and increased flexibility. We’re bringing this same powerful paradigm to real time data processing with Flink in Confluent Cloud. Using this model, users can focus on writing business logic instead of managing nodes and other infrastructure.
Attendees will learn the benefits of serverless and see how it fits into the context of stream processing. We’ll then kick off a demo where we’ll focus on a real world production use case that uses Flink jobs to power an application with extremely low latency.
Flink Forward Berlin 2024 Sessionize Event
Current 2023: The Next Generation of Kafka Summit Sessionize Event
Mayank Juneja
Product Manager, Stream Processing at Confluent
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