Blog

Announcing the Labelbox connector on Databricks: Productionizing unstructured data for AI and analytics at scale

Labelbox has recently launched a connector between Databricks and Labelbox — the LabelSpark library — so teams can connect an unstructured dataset to Labelbox. With LabelSpark, teams can programmatically set up an ontology for labeling and return the labeled dataset in a Spark DataFrame.

Ontology management with Labelbox

Being able to quickly create, edit, and reuse ontologies is key to faster iteration cycles in machine learning.

Simplify collaboration with Issues and Comments

We’re excited to make collaboration and communication within and across teams in Labelbox even easier with the introduction of Issues and Comments.

4 strategies for successful labeling operations

Develop a labeling operation that serves your enterprise ML team with accuracy and efficiency.

Customizing an optimal training data workflow using an API-first approach

Learn how Cape Analytics, a leading provider of AI-powered geospatial property data used Labelbox's APIs to automate their training data workflows.

Managing a Labeling Team and Building a High-Impact Deep Learning Model for Tree Identification

I participated in an amazing AI challenge through Omdena’s community where we built a classification model for trees to prevent fires and save lives using satellite imagery. Omdena brings together AI enthusiasts from around the world to address real-world challenges through AI models.