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.
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
SomaDetect SomaDetect provides dairy farmers with the information they need to produce the highest quality milk with the resources they have today, for a more sustainable dairy food system. An important part of making this happen is is machine learning and computer vision. We spoke with Bharath Sudarsan, Director of
Lytx® — as in analytics — harnesses the power of video to transform transportation fleets with improved safety, efficiency, productivity and profitability. Lytx pioneered the video telematics category two decades ago when it launched its DriveCam® safety program, and remains the market leader today. With more than 500,000 subscriptions, Lytx protects
Genius Sports is a UK-based sports data company who is leveraging computer vision and deep learning to lead the digital transformation of sports intelligence, working on an international scale with top sports organizations such as MLB, PGA Tour, and the Premier League. This May, the NCAA announced its 10-year partnership
Condé Nast is the parent company to over 20 media brands, including Wired, Vogue, Vanity Fair, and Condé Nast Traveler. It’s hard to imagine the extent and richness of a library that combines all the best of fashion, travel, and other media on the planet, let alone the task
ChallengeDeveloping expert AI systems using Machine Learning (ML) models depends on large amounts of accurate, consistent, and comprehensive training data. Raw data is labeled by experts in the domain of interest to create accurate training data. These labeling tasks must be scaled to produce large amounts of consistent and comprehensive
