Covering everything you need to know in order to build AI products faster.
Labelbox•June 24, 2022
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.
Labelbox•September 29, 2021
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.
Labelbox•April 3, 2020
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•April 16, 2019
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•April 8, 2019
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.
Labelbox•December 12, 2018
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