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Most video AI demos stop at simple playback or offline analysis. Real-time video intelligence at scale requires ingesting streams, processing content, & retrieving meaningful insights instantly.
WHAT YOU'LL BUILD
A working real-time video agent powered by VAST DataEngine.
You'll implement a full pipeline: from ingesting video streams to generating summaries, detecting events, & retrieving relevant moments using embeddings.
By the end, you'll have a system you can run, tweak, & take back to your team, capable of processing video in real time, flagging key events, & integrating with downstream tools like Slack.
Your pipeline will:
Ingest video via event-driven triggers (S3 buckets)
Generate LLM-powered video summaries
Detect events from video streams
Create video embeddings for semantic search
Retrieve relevant video segments using vector search
Send automated notifications for key events
KEY TOPICS
Event-driven architectures for video processing
Building with VAST DataEngine for AI pipelines
LLM-based video summarisation
Video embeddings & vector search
Designing scalable, real-time video pipelines
Translating prototypes into production systems
AGENDA
4:00 PM - Doors Open: Welcome & Check-In
Security check-in - elevator to 7th floor - grab a coffee/water/soda
4:30 PM - Framing & Vision: What We're Building & Why
4:45 PM - Live Demo: End-to-End Video Agent in Action
5:00 PM - Guided Build Part 1: Core DataEngine Foundations
(Connect to VAST lab, trigger functions, LLM integration)
6:00 PM - Break
6:10 PM - Guided Build Part 2: Production Features
(Video embeddings, vector queries, user-facing applications)
6:55 PM - Production Wrap-Up: Scaling to Real-World Systems
7:10 PM - Q&A & Next Steps
7:25 PM - Networking with Peers & the VAST Team
8:00 PM - Event Close
LEARNING OUTCOMES
By the end, you'll be able to:
Explain how VLM-powered video agents work in real-time production environments
Use VAST DataEngine to build scalable pipelines for video ingestion & processing
Implement an end-to-end workflow: ingest process summarise embed retrieve
Apply vector search to surface relevant insights from large-scale video data
Design event-driven architectures for automating video intelligence systems
Understand how to take a prototype & extend it into a production-ready setup
Confidently adapt & reuse the starter repo for real-world use cases
WHO SHOULD ATTEND
Intermediate to senior developers, ML/AI engineers, agent builders, & data engineers.
Industries: AI, Media & Entertainment, Financial Services
PREREQUISITES
Required:
Laptop
Comfortable coding in Python
Familiarity with APIs & basic ML workflows
Helpful (not required): Experience with LLMs, embeddings, or event-driven systems
Setup: You'll connect to the VAST lab environment (no local setup required). Instructions sent 3-5 days before the workshop.
Seats are limited: register now to secure your spot!
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