About us
The MLOps Community fills the need to share real-world Machine Learning Operations best practices from engineers in the field. While MLOps shares a lot of ground with DevOps, the differences are as big as the similarities. We needed a community laser-focused on solving the unique challenges we deal with every day building production AI/ML pipelines.
We’re in this together. Come learn with us in a community open to everyone. Share knowledge. Ask questions. Get answers.
You can check out our Slack or podcast that’s filled with tips and tricks to overcoming the common obstacles we’ve all hit in the real world. Make sure to join the #nyc channel.
Find the solutions you need. Share, learn, and grow with us, as we work to bring standardization to the chaotic world of MLOps
Upcoming events
1
Building Real-Time Video Agents with VAST Data Engine
New York Stock Exchange, 11 Wall Street, New York, NY, USNOTE: REGISTER HERE
PLEASE REGISTER ON LUMA TO BE ACCEPTED.
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.
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## 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
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## 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
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## 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***
## 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.***
PLEASE REGISTER ON LUMA
https://luma.com/h8muplvs
https://luma.com/h8muplvs
https://luma.com/h8muplvsSeats are limited: register now to secure your spot!
8 attendees
Past events
26
