Building Feedback-Driven Annotation Pipelines for End-to-End ML Workflows

Build better computer vision models.

Curate | Annotate | Evaluate

Resources:

pip install fiftyone

Virtual

Americas

Launch Event

Building Feedback-Driven Annotation Pipelines for End-to-End ML Workflows – February 18, 2026

Feb 18, 2026

10 - 11 AM PST

Online. Register for the Zoom!

About this event

Companies have been labeling way more data than they actually use. By some estimates, organizations never use over 95% of their data annotations! This over-annotation challenge is compounded by data labeling workflows isolated from the rest of ML tasks, which adds coordination overhead across annotation services, domain experts, and tools.

In this technical workshop, we’ll show how to build a feedback-driven annotation pipeline for perception models using FiftyOne. With 2D and 3D image datasets, 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.

Get hands-on with this step-by-step Getting Started Tutorial.

For a detailed breakdown of the over-annotation problem and how feedback-driven pipelines reduce waste, read our blog: Curate First, Annotate Smarter: Efficient ML Workflows with FiftyOne.

Host

What you’ll learn:

In this session, we’re going to build a complete curate-annotate-train-evaluate loop. We’ll focus on the specific logic that prevents you from wasting budget on the wrong data.

You’ll leave with the code to:

Label fewer images for similar performance: Random sampling is inefficient. We’ll use zero-shot selection and embeddings to mathematically identify the most unique samples in your dataset and allow you to maximize model coverage with a fraction of the usual labeling budget.
Speed Up QA: You’ll learn to annotate and validate labels directly within FiftyOne, and use patch views to review specific objects and fix errors orders of magnitude faster than standard review.
Build a hybrid data selection strategy: Most pipelines either label randomly (which is inefficient) or only chase failure cases (which makes the model forget normal cases). We’ll implement a balanced 30/70 split: 30% for diversity and 70% for targeting specific errors.
Fix your data splits: It’s easy to accidentally cheat on your metrics. We’ll set up a rigorous workflow with a frozen test set for final scores and a separate golden set to catch label drift.
Debug with embeddings: Model performance metrics don’t necessarily tell you what to fix. We’ll use embeddings to visualize the specific clusters confusing your model so you can target those exact scenarios.

The Result: You’ll have a repeatable pipeline that helps you improve model performance with fewer labels, rather than just throwing more data at the problem.

Who should attend:

ML engineers, data scientists, AV/ADAS and robotics practitioners, computer vision researchers, data platform and MLOps engineers, and technical leads responsible for labeling, developing multimodal datasets and models, or maintaining consistent label semantics across projects and tools.