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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.

Labelbox February Update

February's new feature releases bring you better collaboration, increased project efficiency, and the ability to safely extend authorization to private cloud data.

Model Predictions, Semi-Automatic Labeling and Quality Assurance in Production

Model predictions play two vital roles in a machine learning pipeline. Predictions can be used to accelerate labeling speed as well as test and improve the accuracy of production models.

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.

Labelbox January Updates

Learn how Labelbox can improve the way you manage your training data with new features, available now! Introducing: Benchmarks, Label Contains Search, Asset Info, API Key Management, and Bulk Import.

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