Blog

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.

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.

February updates

We’ve focused recent product development on improvements to streamline project management and workflow capabilities with Filtered Exports and Project Tags. We're also introducing Batches, so users can easily select and prioritize data within their labeling queue.

How to create high-quality training data

Improving the quality of training data can mean something different for every use case, model, and even iteration cycle.

January updates

We’ve made improvements to Model Diagnostics that give users access to more insightful comparison metrics and support for additional data types. We’ve also expanded model-assisted labeling (MAL) to support video bounding boxes, and are excited to offer MAL for all paid customers.

Stop labeling data blindly

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.

CAPE Analytics tackles catastrophe and property analyses for insurance with AI

CAPE Analytics creates AI applications, primarily for insurance organizations, that extracts information from aerial or satellite geospatial images.

Genentech develops breakthrough labeling process for medical imagery ML

Genentech researchers are building convolutional neural networks to help diagnose illnesses and aid medical professionals.

Labelbox lands in London: The key to a performant ML model lies within your training data

In a data-centric approach to machine learning, no element is more essential in your ML endeavors than creating and maintaining high-quality training data. Many ML teams fail due to two common but critical mistakes.