How to Improve Data Labeling Efficiency with Auto-Labeling, Uncertainty Estimates, and Active Learning
In this whitepaper, we dive into the machine learning theory and techniques that were developed to evaluate our auto-labeling AI. More specifically, how the platform estimates the uncertainty of auto-labeled annotations and applies it to active learning. This whitepaper will help you measure and evaluate how much you can trust the model output when utilizing auto labeling for data annotation.
We’ve built a platform for everyone involved in the journey from training to production - from data scientists and engineers to ML engineers, product leaders, labelers, and everyone in between. Get started today for free and see just how much faster you can go from ideation to precision models.