Labelbox October Updates
Introducing Python SDK beta
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.
Announcing $10M Series A Funding for Labelbox
Dear Labelbox Community, Today, we’re excited to share a significant update: we recently secured a Series A funding round of $10 million. Our lead investor for this new phase of our company is Gradient Ventures, Google’s AI-focused venture fund, and we thank them for their support. We also
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 Measure Quality when Training Machine Learning Models
With quality assurance processes data scientists can monitor overall consistency and accuracy of training data, quickly troubleshoot quality errors, improve instructions, on-boarding, and training of labelers, and better understand the project specifics on what and how to label.
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
Labelbox December 2018 Updates
Outsourced labeling services are often an instrumental part of making an AI project successful. But at the same time, the accuracy and consistency of the labeling work are critical to training performant AI. To address these two (often competing) needs, it’s now possible to have an outsourced labeling team