June 30 - Beyond Annotation Tools: Building a Complete Physical AI Data Engine

In this workshop we’ll demonstrate workflows for image and video annotation, instance segmentation, polylines, QA and review, collaborative labeling operations in FiftyOne, and smart data selection strategies that help teams reduce wasted labeling spend.

Date, Time and Location

Jun 30, 2026
9 AM PST
Online. Register for the Zoom!

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.

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.

What You’ll Learn

• 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
• 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, including training reusable “Labeling Agents”, prompting with text + visual examples, iterating on outputs before deployment and running large-scale auto-labeling workflows