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How to Annotate the Right Data and Maximize Model Performance - June 30, 2026
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Jun 30, 2026
9 AM PST
Online. Register for the Zoom!
About this event
Organizations waste more than half of their annotated data despite significant investments in collection and labeling. While large-scale data collection is often seen as the bottleneck to building high-performing AI systems, the bigger challenge is ensuring the right data gets labeled and labeled correctly.
Your model performance is ultimately driven by the quality, coverage, and observability of training data. Labeling mistakes, missing scenarios, and limited visibility into model failures lead to wasted annotation spend and time.
In this hands-on workshop, we'll explore how to build an efficient, end-to-end annotation pipeline that improves data quality and downstream model performance. You'll learn how to combine intelligent data selection with automated labeling and model analysis to create efficient annotation operations.
Host
Topics include how to:
- Select the most valuable data for labeling using smart curation techniques
- Save labeling time and costs by using models, agents, and auto-labeling techniques for 2D, 3D, and video data.
- Measure labeling velocity and efficiency to speed up labeler-expert collaboration
- Build customized annotation workflows tailored to your team
- Speed up labeling with collaborative review cycles and QA
- Train and evaluate models, and iterate on improving data coverage and model performance
- Gain insights into which scenarios matter the most and create a feedback loop for labels.
Whether you're annotating in-house or managing an external labeling team, you'll leave with a practical framework and best practices to iterate faster and ship high-quality models, without scaling resources and labeling costs.
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
Resources:
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