About us
đź–– This virtual group is for data scientists, machine learning engineers, and open source enthusiasts.
Every month we’ll bring you diverse speakers working at the cutting edge of AI, machine learning, and computer vision.
- Are you interested in speaking at a future Meetup?
- Is your company interested in sponsoring a Meetup?
This Meetup is sponsored by Voxel51, the lead maintainers of the open source FiftyOne computer vision toolset. To learn more, visit the FiftyOne project page on GitHub.
Upcoming events
8
- Network event
Aug 11 - Debugging Physical AI Models at Scale with Multimodal Data Workshop
·OnlineOnline201 attendees from 52 groupsJoin Voxel51 for a live workshop on how multimodal data workflows in FiftyOne help teams inspect, search, and debug complex Physical AI datasets and explain black-box model behavior at scale. We’ll show how teams can work with synchronized video and sensor data, query for similar scenarios across their datasets, and uncover patterns behind model failures faster than playback-only visualization tools allow.
Date, Time and Location
Aug 11, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!As robotics and autonomous vehicle teams move from traditional perception models to end-to-end Physical AI systems, understanding model behavior is becoming harder than ever. These models ingest synchronized inputs from cameras, sensors, and other data streams, but their decisions can be difficult to explain, reproduce, and improve.
You’ll learn how to use multimodal data to investigate questions like: when did the model swerve, miss an object, misinterpret a scene, or behave unexpectedly — and how can you find every similar moment across your dataset?
Designed for robotics, AV, and machine learning teams, this session will show how FiftyOne helps turn multimodal data into a scalable workflow for model evaluation, debugging, and improvement.
4 attendees from this group - Network event
Aug 13 - How to Build Vision Data Agents with Tools, Skills, and MCP
·OnlineOnline278 attendees from 52 groupsIn this session, you’ll learn how to build production-ready AI agents that can reason over your data, automate complex tasks, and integrate seamlessly into your existing stack using tools, skills, and the Model Context Protocol (MCP).
Date, Time and Location
Aug 13, 2026
9:00 AM - 10:00 AM PST
Online. Register for the Zoom!We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow.
By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle.
Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started.
About the Speaker
Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow, Docker, and OpenCV. I started as a software developer, moved into AI, led teams, and served as CTO. Today, I connect code and community to build open, production-ready AI, making technology simple, accessible, and reliable.
5 attendees from this group 
Aug 13 - From Cold Pool to Hot Queue: Annotation Curation with FiftyOne Workshop
Microsoft NERD New England Research & Development Center, One Memorial Drive, Cambridge, MA, USPre-registration is mandatory to clear building security
Date, Time and Location
Aug 13, 2026
5:30 PM - 8:30 PM ET
Microsoft Research Lab – New England (NERD) at MIT
Deborah Sampson Conference Room
One Memorial Drive, Cambridge, MAIn this hands-on workshop, you'll use FiftyOne to run the full rare-class mining loop end-to-end on a large unlabeled image pool: compress the pool with near-duplicate detection, embed images with a modern vision backbone, mine candidate positives via seeded similarity from a tiny labeled set, confirm them through targeted human review, and prioritize the survivors for annotation using representativeness and uniqueness scores. You'll then fine-tune a detector on the prioritized examples and come back to FiftyOne to verify the missed-mode clusters actually got covered.
What You'll Walk Away With
- A working FiftyOne pipeline for finding rare classes in any visual dataset you own
- A repeatable four-stage funnel — compress, mine, confirm, prioritize — with a clear objective at each stage
- A fine-tuned detector that demonstrably outperforms one trained on the same number of randomly sampled images
- The mental model that data curation — not architecture or hyperparameters — is the highest-leverage thing you can do to improve a rare-class detector
8 attendees- Network event
Aug 20 - Cold Pool to Hot Queue: Annotation Curation with FiftyOne
·OnlineOnline33 attendees from 48 groupsIn this hands-on workshop, you'll use FiftyOne to run the full rare-class mining loop end-to-end on a large unlabeled image pool: compress the pool with near-duplicate detection, embed images with a modern vision backbone, mine candidate positives via seeded similarity from a tiny labeled set, confirm them through targeted human review, and prioritize the survivors for annotation using representativeness and uniqueness scores.
Time, Date and Location
Aug 20, 2026
9:00 AM - 11:00 AM PST, 2026
Online. Register for the Zoom!What You'll Walk Away With
- A working FiftyOne pipeline for finding rare classes in any visual dataset you own
- A repeatable four-stage funnel — compress, mine, confirm, prioritize — with a clear objective at each stage
- A fine-tuned detector that demonstrably outperforms one trained on the same number of randomly sampled images
- The mental model that data curation — not architecture or hyperparameters — is the highest-leverage thing you can do to improve a rare-class detector
Past events
256
