Machine learning is widely understood by the software community. But it is still hard to build a company around machine learning, because there is not easy access to large, unique data sets.

Scale is a platform for training and validating data that is used for machine learning.

Most machine learning models are built with supervised learning. Labeled examples are analyzed to understand the mathematical correlations between those labels. The more labeled training examples there are, the more accurate the correlations will be. 

Today, we have high quality frameworks for writing the models. We have cheap cloud computing for training and deploying the models. The biggest factor that is preventing a wide variety of potential machine learning applications from existing is lack of access to large, labeled data sets.

Scale gives developers an API for labeling images, sound, natural language, and video. Scale is used by self-driving car companies, Airbnb, OpenAI, retailers, and robotics companies. The product is used broadly and at high volume. Scale was started only three years ago, and recently raised $100m at a valuation above $1b, making it one of the fastest growing software companies in history.

Alexandr Wang joins the show to discuss how Scale works, the future of machine learning, and the future of work. He also describes the complexities of building Scale, and how he manages his own psychological state.