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Pixop CTO Jon Frydensbjerg to speak at NAB 2026 BEIT Conference on real-world ML deployment in streaming

Pixop CTO Jon Frydensbjerg to speak at NAB 2026 BEIT Conference on real-world ML deployment in streaming

Pixop co-founder and CTO Jon Frydensbjerg will join a panel of industry practitioners at the NAB Show 2026 Broadcast Engineering and IT (BEIT) Conference to discuss how machine learning is moving from experimentation to measurable impact across video workflows.

Pixop co-founder and CTO Jon Frydensbjerg will join a panel of industry practitioners at the NAB Show 2026 Broadcast Engineering and IT (BEIT) Conference to discuss how machine learning is moving from experimentation to measurable impact across video workflows.

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Benjamin Schwarz

Posted originally

The session, Enhancing AI-Powered IP Video Workflows and Experiences: From Production to Delivery, is organized by the Streaming Video Technology Alliance (SVTA) and moderated by Thierry Fautier, Managing Director of Your Media Transformation. Panelists include Faisal Ishtiaq (Consultant), Guido Meardi (V-Nova), Pierre-Alexandre Entraygues (Hexaglobe / BBright), Pepijn Tijhuis (Unified Streaming), and Yasser Syed.

The discussion focuses on what is working in production environments today. Topics include automated captioning, real-time video enhancement, content steering, dynamic ad selection, and metadata generation at scale, with a shared focus on reducing manual effort, improving workflow efficiency, and delivering consistent results.

For Pixop, this reflects a core area of focus. Pixop LIVE sits between contribution and encoding, normalizing inconsistent live inputs into a consistent, high-quality signal in real time, without adding workflow complexity.

Jon brings the perspective of a team that has spent over six years building and deploying machine learning models for live video environments, where latency, stability, and integration constraints are non-negotiable.

This session highlights a broader industry shift toward practical, workflow-integrated machine learning, where performance is measured not by models, but by outcomes.

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