Under the Hood of Uber ATG’s Machine Learning Infrastructure
Managing multiple machine learning models to enable self-driving vehicles is a challenge. Uber ATG developed a model life cycle for quick iterations, continuous delivery, and dependency management. As Uber experienced exponential growth over the last few years, now supporting 14 million trips each day, our engineers proved they could build for scale.
That value extends to other areas, including Uber ATG (Advanced Technologies Group) and its quest to develop self-driving vehicles. A significant portion of this work involves creating machine learning (ML) models to handle tasks such as processing sensor input, identifying objects, and predicting where those objects might go. The many models needed to solve this problem, and the large team of engineers working on them, creates a management and versioning issue in itself.