Active Learning

Labelbox • May 4, 2022

Catalog: The launchpad for managing and curating unstructured data

Labelbox built Catalog, a unifying layer across your ML data, to tackle managing massive unstructured datasets, visualizing and exploring data, and curating efficient batches of high-value data.

Active Learning

Labelbox • Mar 11, 2022

How to use embeddings to create high-quality training data

Functions and embeddings are a powerful way to quickly search and explore your unlabeled and labeled data. Using them can help you break down silos across datasets so you can focus on curating and labeling the data that will most significantly improve model performance.

Active Learning

Labelbox • Oct 8, 2021

How to improve model performance with active learning

Let's dive into some of the specific challenges of data-centric ML and how your team can address them by employing active learning methods.

Tutorials

Randall Lin • Apr 30, 2020

Active Learning: Speed up training data cycles with uncertainty sampling

Machine learning engineers often rely on an active learning framework to surface the most informative data to learn from.

Catalog: The launchpad for managing and curating unstructured data

6 fundamental sessions on improving your AI data

Streamlined data management and key editor improvements

March updates

Catalog: The launchpad for managing and curating unstructured data

How to use embeddings to create high-quality training data

How to improve model performance with active learning

Active Learning: Speed up training data cycles with uncertainty sampling