Blog

3 Things we learned at CVPR 2022

This year’s CVPR conference featured over two thousand papers, showing a few emerging themes and trends in the computer vision field.

6 key best practices for AI teams to save time when creating AI data

Wading through a vast amount of unstructured data to accurately annotate assets requires a tremendous amount of patience, organization, and time. Learn six key time-saving practices for ML teams to implement when handling AI data.

Why your ML team needs a data engine

Learn how a data engine can help your team save time throughout the training process and deliver more measurable ROI for your ML efforts.

Labelbox lands in London: The key to a performant ML model lies within your training data

In a data-centric approach to machine learning, no element is more essential in your ML endeavors than creating and maintaining high-quality training data. Many ML teams fail due to two common but critical mistakes.

Automating Computer Vision Annotation: Let Your Model Do The Work.

Discover the 3 basic ways to automate labeling depending on how aware the method is of the data to be labeled.

All hands on deck: A student team's annual quest to build the world's best autonomous underwater robot with AI

The story of one team's quest to win the annual RoboSub competition using the latest advances in AI and training data as part of their advantage.

Introducing the Labelbox NLP labeling editor: sign up for early access

We’re excited to announce the closed beta of our Natural Language Processing (NLP) product: a brand new Named Entity recognition (NER) and text classification labeling system. Labelbox customers are among the largest enterprises that are rapidly growing their machine learning practices and want a unified platform to create and manage all of their training data. A lot of their real-world machine learning problems require both computer vision and natural language processing models to work in co

Labelbox Adapts to Support American Family Insurance Automation

In this article, we discuss why and how we built a new labeling ontology feature to support American Family's use case. Labeling ontology is critical for machine learning applications. It determines what the labeler can label and, in turn, the categories the model will be able to identify.

Labelbox Speaks on Ethics of AI at O'Reilly's Strata Data Conference

"Is your AI really making good decisions or have you built a deceptive black box that reinforces ugly stereotypes?"  asked  O'Reilly's Ethics Summit. At this Strata Data conference, Labelbox Co-founder & COO, Brian Rieger, gave an answer for reducing bias in machine learning.

How to Scale Training Data

A Guide to Outsourcing Without Compromising Data Quality In order for data science teams to outsource annotation to a managed workforce provider — also known as a Business Process Outsourcer (BPO) — they must first have the tools and infrastructure to store and manage their training data. Data management tools and infrastructure should support R&D product management teams, outsourced labeling teams, and internal labeling and review teams working together in a single centralized place with fully