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.
they must all die!
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.

and what the fuck it this?
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.

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.

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.

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.

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