5 posts with this tag
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
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 QualityIn 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