Covering everything you need to know in order to build AI products faster.
How to evaluate and optimize your data labeling project's results
Learn how to evaluate the results of your labeling project in order to further optimize and improve future iterations and batches of data.
How to define a task for your data labeling project
Learn how to align on key components of your project: define a task, create an ontology, and determine timelines for your labeling project.
How to find similar data in one click
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.
Get started with active learning
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.
How to scale up your labeling operations while maintaining quality
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.
How to customize your annotation review process
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.
How to prepare and submit a batch for labeling
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.
A new way to queue & review
A migration guide for the switch to Batch-based queueing, Workflows, and the Data Rows tab.
How to find and fix label errors
Learn how you can use Labelbox Model to visually compare your ground truths and predictions to identify and fix label errors.
How to find and fix model errors
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.
