Cedric Clyburn (@cedricclyburn), Senior Developer Advocate at Red Hat, is an enthusiastic software developer with a background in Kubernetes, DevOps, and container tools. Focused on open-source software, he both contributes (e.g., Podman, vLLM) and enjoys speaking, with prior experience at Devoxx, WeAreDevelopers, The Linux Foundation, and more. Cedric also spends (too much) time creating video and written content helping developers learn new topics in emerging technologies, with over 2M+ views
English session - Beginner
You've likely heard how rising AI bills have made teams start thinking about running their own open-weight models. The good news? You can achieve near-frontier capabilities, but the hard part is figuring out the optimizations that are right for your hardware, from Mac's to NVIDIA GPU's. I'll walk you through what works best, from quantization to speculative decording, through live demos that balance throughput, latency, and accuracy.
English session - Beginner
When an AI agents goes wrong, it doesn't fail in one log line. It's across a chain of model decisions, retrieval, retries, and tool calls that jump between services. So, how do we tell what happened with each request, and perhaps why the response wasn't what we wanted? This session is a demo of tracing AI agents using OpenTelemetry and MLflow to understand requests from user -> agent -> tools -> and back, to better understand agent behavior.