Labelbox May Update
Last three months brought many new features and updates to Labelbox. We have 5 most exciting things to share with you.
(1) Announcing $10M in funding
We're excited to announce that Labelbox has raised $10M in Series A funding. The financing round was led by Gradient Ventures, Google's AI focussed fund and joined by iconic firms Kleiner Perkins and First Round Capital.
We are inspired by our customers and passionate users around the world who are using Labelbox to build great machine learning applications. This round of financing enables us to accelerate making Labelbox the best visual training data software for computer vision teams.
Read more in the full announcement.
2) Predictions: Model based pre-labeling
Why label data from scratch if your model can help? Labelbox now allows you to import model predictions as pre-labels via the Prediction API.
The result is a significant reduction in the time and cost of human labor needed for labeling. The model generated pre-labels are imported into Labelbox via the Prediction API and then presented as an initial recommendation for each labeling task. This transitions the focus of the labeling teams from zero-to-one-labeling tasks to QA. You can expect up to a 5x increase in labeling speed for bounding boxes and classification tasks by operating a semi-automated labeling workflow in this manner.
(3) Labelbox Workforce
Labelbox now offers a fully managed and dedicated workforce to all customers. With transparent pricing and a dedicated project manager, your team can safely rely on Labelbox for a fully integrated training data solution. This workforce offering is available in addition to the workforce providers already available on Labelbox, including CloudFactory, Daivergent, Cogito, Edgecase and iMerit. As a Labelbox customer, you can connect to one or more of these workforces right inside your projects.
With the Labelbox Workforce, machine learning teams have access to a dedicated labeling team with whom they can collaborate and train for their use cases. Together with pre-labeling and automated quality control features, Labelbox customers are now spending much less on human labor and creating high-quality training data at scale.
(4) Real-time workflow
Labelbox Real-time enables teams to use a concurrent labeling task distribution and queue system in the loop with production applications. In other words, raw data can now be streamed directly into Lablebox to be labeled by the labeling team that is working 24/7. The labeled data is sent back within a specific time (as quickly as a few minutes!) to the application. Meet stringent SLAs and tight turn arounds on your production system with Labelbox Real-time. This feature is available to all users starting today.
(5) See you at CVPR, June 2019
Here's to an exciting summer ️filled with the joy of not building or maintaining training data infrastructure! We hope to see you at the Computer Vision and Pattern Recognition Conference in Long Beach on June 16-20th.