Covering everything you need to know in order to build AI products faster.
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.
Labelbox•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.
Labelbox•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.
Labelbox•November 7, 2022
The Data Rows tab is the central hub for all data rows within a given project. You can view, manage, and filter for data rows within your project to better prioritize data for labeling and to accelerate model development.
Labelbox•November 5, 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.
Labelbox•November 4, 2022
A migration guide for the switch to Batch-based queueing, Workflows, and the Data Rows tab.
Labelbox•November 2, 2022
What is changing? Labelbox is deprecating QueueMode, meaning all projects will be required to use Batch-based queueing for all projects by the end of Q1 2023. We will release a new SDK version that will automatically set up all new projects with this new functionality. Labelbox will release the following changes: Free / EDU / Starter customers can expect the changes on November 21st. Pro and Enterprise customers can expect the changes on a rolling basis starting December 14th. Automatic up
Labelbox•October 28, 2022
Learn how you can use Labelbox Model to visually compare your ground truths and predictions to identify and fix label errors.
Labelbox•October 10, 2022
Learn how you can use Model to configure, track, and compare essential model training hyperparameters alongside training data and data splits. Easily track and reproduce model experiments to observe the differences and share best practices with your team.
Easily turn stores of documents and PDF files into performant ML models with our Document editor. With the ability to use an NER text layer alongside OCR techniques, teams can annotate text, images, graphs, and more without losing context.
Labelbox•October 9, 2022
