​AI demos are everywhere.

​But how do companies actually build AI products that work reliably with live data, low latency, and thousands of users?

​Join engineers as they share the architectures behind production AI systems - from streaming data and event-driven architectures to real-time databases, RAG pipelines, observability, and open-source infrastructure.

​No hype. Just practical engineering stories, lessons learned, and opportunities to meet others building modern data systems.

​Food and drinks included 🍻

​

​PS. Want to hear more great speakers? Sign up for the Open Source Analytics and AI Conference on Nov 2 (online and in-person). Register here.

​

​Agenda

• ​6 pm - Networking

• ​6:15 - 8:00 pm - Talks

• ​8:00 - 9:00 pm - Networking

​Speakers

• ​Robert Hodges, CEO @ Altinity

• ​Goutham Veeramachaneni, Staff Software Engineer @ Grafana

• ​Aashish Nair @ dltHub

​Description of talks

​​Vibe building AI applications with confidence

​Speaker: Goutham Veeramachaneni, Staff Software Engineer @ Grafana

​Abstract: I am no AI engineer, however, I am building a lot of small home-cooked apps for my specific use-cases. I also am at a point that I don't read the code anymore for these (hides shame). The only saving grace here is that I ensure proper observability is baked in from the get-go and continuously use the observability data and then use the observability data to continuously improve the applications.

This practice is borrowed from how we build our Grafana Assistant and we'll dive into a practical demo of how I improved one of my LLM powered apps, and then share how you can do the same for yours.

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​Ingesting Agent Traces with dltHub

​Speaker: Aashish Nair @ dltHub

​Abstract: coming soon

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​Altinity talk: coming soon!

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