How do you bring real-time data to AI and machine learning models?
Kai Waehner, Field CTO at Confluent, explains how to use Flink for real-time model inference to power use cases for generative AI, real-time analytics, predictive maintenance, and more.

RESOURCES
► Docs: https://docs.confluent.io/cloud/current/ai/ai-model-inference.html
► GenAI hub: https://www.confluent.io/generative-ai/
► Flink for retrieval-augmented generation (RAG): https://www.youtube.com/watch
► How Flink works: ​​https://www.youtube.com/watch
► Get Started free on Confluent Cloud: https://www.confluent.io/get-started/

CHAPTERS

00:00 - Model Inference

03:35 - Implementation with a Data Streaming Platform

04:01 - Event-driven Architecture

05:09 - Benefits

06:19 - Stream, Connect, Process, Govern

08:12 - Predictive AI

08:56 - Generative AI

09:22 - Retrieval-augmented generation (RAG)

11:59 - Predictive Maintenance for Manufacturing

ABOUT CONFLUENT
Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations. To learn more, please visit www.confluent.io.

#confluent #apachekafka #kafka #apacheflink #flink