# Building Real-Time Video Agents with VAST Data Engine
**Date de l'événement :** 11/05/2026
* Publié le 11/05/2026

### Date
11/05/2026

### Galerie d'image
![1.png](https://firebasestorage.googleapis.com/v0/b/memory-ai.appspot.com/o/prod%2FrKxsdSTpqCfzIFY8Y2hg%2FprojectsMedias%2FwsXG3WWRSdDZVRCXOCOB%2Fthumbs%2F1_1600x900.png?alt=media&token=d4578213-5001-4020-b6ad-4a5965cb1e0d) 

### Ville
`#New York` 

## Description
Most video AI demos stop at simple playback or offline analysis. Real-time video intelligence at scale requires ingesting streams, processing content, and 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, and retrieving relevant moments using embeddings.  
By the end, you'll have a system you can run, tweak, and take back to your team, capable of processing video in real time, flagging key events, and 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 and 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 and 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 and 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 and 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 and extend it into a production-ready setup  
Confidently adapt and reuse the starter repo for real-world use cases  
  
WHO SHOULD ATTEND  
Intermediate to senior developers, ML/AI engineers, agent builders, and data engineers.  
Industries: AI, Media & Entertainment, Financial Services  
  
PREREQUISITES  
Required:  
Laptop  
Comfortable coding in Python  
Familiarity with APIs and 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!

**Lien de l'évènement :** [https://luma.com/h8muplvs](https://luma.com/h8muplvs)

### Pays
`#United States` 

### Continent
`#North America` 

**Médias associés :**
[Média 1](https://80954c1d.sibforms.com/serve/MUIFABojU8UBbDiX_TdcGa7Wv5VMoVB_nBZ92mkLkGlS1pJLpP7s-pVJusyN-7cG9KPrSuv3fv7TmXwuw_AoyNUShR8jZhmNDgUbZPJO2V5xYXlNz4YXOTjSb8X7Lj7PRIPzgzEWlLbA4f4uw_F8RM51EUsjSfQQko0qaby98GHMdYJVWLIXd5JzzaXBGmqN2CcYOFuqnbnaYEnw) 

## event_id
evt-QCanrGxP43VkFOv@events.lu.ma

### Outils
`#VAST DataEngine` `#Slack` `#S3` `#Python` 

### Selection
`#Creativity Selection` 



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