Introducing Recursion: the RL platform for enterprise specialist agents
Covering everything you need to know in order to build AI products faster.
Learn how to evaluate the results of your labeling project in order to further optimize and improve future iterations and batches of data.
•January 12, 2023
Learn how to align on key components of your project: define a task, create an ontology, and determine timelines for your labeling project.
Powerful similarity search capabilities can give your team an edge by helping find specific data points in an ocean of data. Learn more about how to find similar data in one click with Labelbox.
•December 19, 2022
Discover how to get started with active learning by leveraging the 3 techniques that consistently help ML teams more quickly identify what data will most dramatically improve model performance.
•November 16, 2022
Many ML teams are eager to label all their data at once. However, this can actually increase time and cost. Learn how you can effectively build an iterative approach to your labeling operations to ensure quality while scaling.
•November 8, 2022
Custom workflows can help optimize how labeled data gets reviewed across multiple tasks and reviewers. Workflows is a new feature that allows teams the flexibility to tailor their review workflows for faster iteration cycles.
•November 7, 2022
High-quality training data is crucial to the success of any ML project. Rather than queueing an entire dataset for labeling, queuing Data Rows with batches gives teams greater control and flexibility in the prioritization of a project’s labeling queue.
•November 4, 2022
A migration guide for the switch to Batch-based queueing, Workflows, and the Data Rows tab.
•November 2, 2022
Learn how you can use Labelbox Model to visually compare your ground truths and predictions to identify and fix label errors.
•October 10, 2022
A great way to boost model performance is to surface edge cases on which the model might be struggling. You can fix those model failures with targeted improvements to your training data so that the model is better trained on these edge cases.
•October 5, 2022
