Blog

How to debug machine learning models

Creating a high-performing model isn't easy, there are many factors to consider, including debugging. Learn how to debug machine learning models.

Training a model from your labeled data in just one click

Labelbox recently launched the beta for our model training and experiment management tools to help ML developers with 1-click model training using Vertex AutoML.

Why data quality is important for machine learning

Why is data quality important in machine learning? Your model is only as good as the quality of its data. Read on to learn more.

How to use Hugging Face 🤗 models to jumpstart model training

Using a pre-trained model like Hugging Face means less training and less effort in building your model's architecture. Read on to learn more.

Increase labeling velocity and optimize workflows with our newest features

We’ve continued to focus product development to help teams increase labeling velocity and optimize their data management workflows. We’re also excited to announce key improvements to our editors to better support text and documents.

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.

6 fundamental sessions on improving your AI data

Join us at Labelbox Blueprint on May 18th - A virtual event covering six fundamental sessions to improve your AI data

Streamlined data management and key editor improvements

We've focused recent product development on streamlining essential data management workflows and are introducing new collaboration tools for labeling operations and labeling teams. We’ve also made some key updates to our Editors that enable more complex ML use cases and improve usability.

March updates

We’ve focused recent product development on improvements to Catalog, the ability to filter data rows by status, updates to our account usage page, and more.

The training data practices used by the most successful AI teams

Explore best practices to reduce the amount of time AI teams need to spend on training data.