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.
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.
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
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.
"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.
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
