Day 1: Conference Day

Audiences are engaging with video across more platforms, formats, and moments than any production team can manually serve — vertical content for social, highlights for engagement, subtitles for accessibility. Until recently, applying AI to live video at broadcast latency was impractical: the computational demands of modern ML models conflicted with the strict real-time constraints of live encoding pipelines. Learn how AWS Elemental has removed that constraint, what becomes possible when content intelligence operates in parallel with live encoding rather than as a post-production step, and how broadcasters are using structured video metadata to drive engagement, unlock new revenue streams, and reach audiences across every screen in real time.

Delivering a seamless playback experience requires a caching footprint that serves different workloads and access patterns using the best-matching operational model. While traditional demand-filled caches remain a popular pattern, Netflix increasingly relies on versioned datasets, snapshot-published with periodic refreshes, to power the streaming control plane. 

This session breaks down four distinct versioned dataset architectures deployed at scale. We will examine how they vary across dimensions of size, read latency, staleness tolerance, and cost, ranging from small in-memory datasets written in near-real-time to distributed petabyte-scale datasets published from offline jobs. Attendees will learn how matching the right caching architecture to specific media workloads (from asset manifests to user state) sidesteps classic failure modes of demand-filled caches such as stampedes and cold-start misses while meeting strict read-performance KPIs.

Validating anti-piracy mechanisms under realistic live conditions is challenging due to the complexity of simulating malicious subscriber pools at scale. Traditional piracy detection relies on evaluating raw CDN logs, introducing data egress costs and delaying mitigation until after the event. 

This session demonstrates an edge-native approach to identifying token abuse in real time. We will show how to design a simulation framework to create pools of valid and pirate users. Then, utilizing edge-level token parsing and time-limited window tracking, we will flag bad actors with high confidence. You'll learn how to design the simulation framework and corresponding orchestration layer with real-time alerts, protecting stream monetization and avoiding overloading the operations center.

Video is token-hungry by nature, directly driving inference cost across compute, memory, and latency at scale. One minute of video at 30fps could represent 1.8 million words, yet the vast majority is spatially, temporally, or perceptually redundant. We argue that AI video token compression shares a profound connection with conventional video compression engineering. RD optimization, motion-compensated prediction, perceptual salience modeling, and ROI encoding all have direct analogs in efficient video tokenization. Further combined with lightweight domain-specialized pre-analyzers and vertical-specific priors, compression-domain thinking offers a practical path to making large-scale multimodal video inference computationally tractable.