Labelbox Briefing Note



Document ID: CGBN159 | Last Updated: Feb. 25, 2020

Machine learning is powering most of the recent advancements in AI, including computer vision, natural language processing, predictive analytics, autonomous systems, and a wide range of applications. However, in order for these systems to be able to create accurate generalizations, these machine learning systems must be trained on data. It goes without saying that machine learning data needs to be clean, accurate, complete, and well-labeled so the resulting machine learning models are accurate. Whereas it has always been the case that garbage in is garbage out in computing, it is especially the case with regards to machine learning data.

Around since early 2018, Labelbox is a training data platform that focuses on providing a platform for supervised learning technology. While not managing their own labor pool, the company provides a complete solution for training data problems with fast labeling tools, human workforce, data management, an API and automation features. The company allows users to select from a range of data labeling labor pools in combination with the company’s Training Data Platform that enables organizations to label data, manage quality, and operate a production training data pipeline. Organizations use Labelbox as an end-to-end platform to create training data, manage the data and process, and support production pipelines with with APIs.

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