Feb 18 - Feedback-Driven Annotation Pipelines for End-to-End ML Workflows

In this technical workshop, we’ll show how to build a feedback-driven annotation pipeline for perception models using FiftyOne. We’ll explore real model failures and data gaps, and turn them into focused annotation tasks that then route through a repeatable workflow for labeling and QA. The result is an end-to-end pipeline keeping annotators, tools, and models aligned and closing the loop from annotation, curation, back to model training and evaluation.

Time and Location

Feb 18, 2026
10 - 11 AM PST
Online. Register for the Zoom!

What you'll learn

• Techniques for labeling the data that matters the most for annotation time and cost savings
• Structure human-in-the-loop workflows for finding and fixing model errors, data gaps, and targeted relabeling instead of bulk labeling
• Combine auto-labeling and human review in a single, feedback-driven pipeline for perception models
• Use label schemas and metadata as “data contracts” to enforce consistency between annotators, models, and tools, especially for multimodal data
• Detect and manage schema drift and tie schema versions to dataset and model versions for reproducibility
• QA and review steps that surface label issues early and tie changes back to model behavior
• An annotation architecture that can accommodate new perception tasks and feedback signals without rebuilding your entire data stack