How to Annotate the Right Data and Maximize Model Performance - June 30, 2026

Name: How to Annotate the Right Data and Maximize Model Performance - June 30, 2026
Start: 2026-06-30
End: 2026-06-30

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