Blog

Introducing Image Segmentation at Labelbox

We set out to create a tool that makes image segmentation fast and accurate to make it accessible to more computer vision teams and projects.

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.

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.

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

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

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

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