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Beyond Annotation Tools: Building a Complete Physical AI Data Engine with FiftyOne - June 30, 2026

Jun 30, 2026

9 AM PST

Online. Register for the Zoom!

About this event

Annotation is no longer just about drawing boxes. Modern physical AI teams need an end-to-end system for labeling, QA, dataset curation, project management, auto-labeling, and video understanding — all tightly integrated into the workflows where models are actually built and evaluated.

In this webinar, Nick Lotz will showcase how FiftyOne has evolved into a complete physical AI platform with comprehensive annotation capabilities built directly into the product. We’ll demonstrate workflows for image and video annotation, instance segmentation, polylines, QA and review, collaborative labeling operations, and smart data selection strategies that help teams reduce wasted labeling spend.

You’ll also get an early look at new agentic labeling workflows powered by “Labeling Agents” — intelligent systems that can learn from text prompts and visual examples to automatically label datasets at scale. We’ll walk through how teams can rapidly create reusable labeling agents, validate outputs, and apply them across large datasets as background tasks.

Whether you’re building computer vision models for robotics, autonomous systems, manufacturing, retail, or multimodal AI applications, this session will show how integrated annotation and data-centric workflows can dramatically accelerate iteration speed while improving dataset quality.

Host

What You’ll Learn

How FiftyOne supports annotation workflows for:
- Classification
- Object detection
- Instance segmentation
- Polylines
- Video detection and tracking
How to create, edit, QA, and manage 2D and 3D labels directly in context
How annotation project management workflows help coordinate labeling teams and reviews
How SAM2-powered click-to-segment workflows enable fast browser-based segmentation
How agentic labeling works:
- Training reusable “Labeling Agents”
- Prompting with text + visual examples
- Iterating on outputs before deployment
- Running large-scale auto-labeling workflows
How smart data selection strategies reduce annotation costs and improve model performance
Why integrated annotation is becoming a core requirement for modern physical AI platforms
How to unify data curation, annotation, evaluation, and model iteration inside a single workflow

Who Should Attend

Machine Learning Engineers
Computer Vision Engineers
MLOps & Data Engineers
Annotation & Data Operations Teams
AI Platform Teams
Technical Leaders building physical AI systems
Teams evaluating annotation infrastructure for production AI workflows