Blog

6 key best practices for AI teams to save time when creating AI data

Wading through a vast amount of unstructured data to accurately annotate assets requires a tremendous amount of patience, organization, and time. Learn six key time-saving practices for ML teams to implement when handling AI data.

Why your ML team needs a data engine

Learn how a data engine can help your team save time throughout the training process and deliver more measurable ROI for your ML efforts.

Catalog: The launchpad for unstructured data management

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.

How to use ML 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.

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.

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.