Blog

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.

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.

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.

Announcing our Series D

We're thrilled to announce that Labelbox has raised $110 Million in Series D financing, led by Softbank's Vision Fund II.

Built for enterprise ML

As ML teams expand and launch new AI initiatives, they have to manage more organizational complexity and need best-in-class enterprise solutions that are flexible, scalable, and secure.

Labelbox December updates

We’ve focused recent product development on enhancing user productivity and usability across core aspects of our platform.

Announcing Labelbox on Databricks Partner Connect

Today, we are thrilled to announce that Labelbox is a launch partner in the Databricks Partner Connect portal.

Labelbox November updates

We’ve enhanced our annotation capabilities with editors to support new data types, improved support for active learning workflows, and other key improvements.

How to optimize your entire ML pipeline

Labelbox has partnered with Snowflake, Databricks, DataRobot, and Google Cloud Platform (GCP) to ensure seamless connections as teams store and manage data as well as train and deploy their models.