ML teams need to know "what data will improve model performance fastest?" These best practices will help teams identify model errors and prioritize better data.

Learn best practices for storing, evaluating, and visualizing labeled and unlabeled data so you can accelerate your model iteration cycles and simplify your training data pipeline.

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.

We’re excited to introduce our updated text editor, which makes it easy for teams to collaborate and use Labelbox for more complex NLP use cases.

It’s now easier than ever to get started with our SDK thanks to simplified documentation and a set of new tutorials.