Blog

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

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 work right inside of your Labelbox project. Let’s take a closer look to see how this works. Inside of a Labelbox project, head to Settings>Collaborators. There you will see the Outsource tab

It All Boils Down to the Training Data

Is your model not performing well? Try digging into your data. Instead of getting marginal improvements in performance by searching for state-of-the-art models, drastically improve your model’s accuracy by improving the quality of your data. Digging Into the Training Data Since most data scientists are adapting off-the-shelf algorithms to specific business applications, one of the most difficult challenges that data scientists face today is creating a continuous workflow that consistently fee

Labelbox August 2018 Updates

Labelbox Raises $3.9M & Product Updates We are excited to introduce teams, customer success stories and $3.9M in seed funding from Kleiner Perkins, Gradient Ventures, Google’s new AI fund and First Round Capital. Read on to learn more. Introducing Teams Building AI often means collaboration from many different functions, including software, management, operations, domain expertise, and data science. For these kinds of environments with internal and sometimes external groups working together,

The Internet of Cows: AI in Agriculture with SomaDetect

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 AI, who went into a little more detail about how SomaDetect is making a splash through the internet of cows. “Our basic business goal is to make better milk. We have IoT devices t

Lytx: Using Data to Help Save Lives on Our Roadways

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 over 3,000 commercial and government fleet clients and 850,000 drivers. The company has analyzed more than 80 billion miles of driving data — addin

Genius: Artificial Intelligence Transformation of Pro Sports

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 with Genius, the goal of which is to develop and implement new data capture and analysis services, coaching insights, and real-time statistics that can be used to improve gamep

Product Updates: July 2018

Labelbox adds Auto Consensus, TensorFlow integration and usability features. We are very thankful to all of our passionate users who have helped us continuously improve Labelbox. Over the past few months, we’ve made a number of usability improvements. Now, we are taking a major step to help our users easily create and manage training data. Read on to learn about new features in Labelbox. Auto Consensus Auto Consensus allows you to quantitatively measure the quality of your training data — th

Condé Nast: AI for World-Class Media

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 of managing it. However, one of Labelbox’s customers is doing just that. We spoke with Paul Fryzel, who is directly responsible for the ongoing research, design, and implementation of Condé Nast’s international media platfor

NTConcepts: Rapid training and deployment of an expert AI system

Challenge Developing 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 training data. It is challenging, however, to implement a scalable data labeling infrastructure while still enabling rapid experimentation. The key challenge