Speaker
Description
In this talk, we will share our experience enabling production-grade Edge AI solutions on top of GStreamer. The discussion goes beyond building working video analytics pipelines and focuses on what it takes to turn them into enterprise-ready systems that can be deployed, operated, monitored, and extended reliably at the edge. We will address this through two complementary dimensions: enterprise-grade deployment and the evolution of Vision AI from classical computer vision toward VLM- and agentic AI-driven workflows.
The enterprise-grade deployment dimension focuses on microservice-style operation: dynamic stream and pipeline lifecycle management, integration with cameras and messaging infrastructure, remote diagnostics, latency tracing, and operational observability.
The Vision AI dimension focuses on the evolution from traditional CV workloads toward VLM and agentic AI scenarios. These workloads introduce new requirements for AI model integration, external AI services, dynamic pipeline reconfiguration, event- or prompt-driven processing, and short-term retention of selected data for retrospective analysis.
Rather than presenting a finished framework, the talk is intended to open a community discussion: which capabilities should remain application-specific, which could become reusable plugins or conventions, and which gaps might be addressed in the GStreamer base repository or surrounding ecosystem.
Proposed 30-minute timeline:
5 min — Introduction and background: why GStreamer is a strong foundation for Edge AI, and how Intel is using it today.
10 min — Enterprise-grade deployment: operating GStreamer-based pipelines as reliable edge services.
10 min — Vision AI evolution: implications of CV, VLM, and agentic AI workflows for model integration, pipeline flexibility, and GStreamer abstractions.
5 min — Summary and Q&A: key takeaways and discussion points for reusable conventions, plugins, or base-repository additions.
Speaker Bio
Tomasz Janczak is a Senior Principal Engineer at Intel Corporation. For the last three years he has been driving Intel Edge AI software efforts, which led to release of Intel Open Edge Platform.
Prior to involvement in Edge AI, he worked on GPU architecture, performance analysis, compilers and networking products.
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