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According to Uber, the most important component of the ATG’s workflow is VerCD, a set of tools and microservices developed specifically for prototyping self-driving vehicles. It tracks the dependencies among the various codebases, data sets, and AI models under development, ensuring that workflows start with a data set extraction stage followed by data validation, model training, model evaluation, and model serving stages.
“VerCD … has become a reliable source of truth for self-driving sensor training data for Uber ATG,” wrote Uber. “By onboarding the data set building workflow onto VerCD, we have increased the frequency of fresh data set builds by over a factor of 10, leading to significant efficiency gains. Maintaining an inventory of frequently used data sets has also increased the iteration speed of [machine learning] engineers since the developer can continue their experimentation immediately without waiting several days for a new data set to be built. Furthermore, we have also onboarded daily and weekly training jobs for the flagship object detection and path prediction models for our autonomous vehicles. This frequent cadence of training reduced the time to detect and fix certain bugs down to a few days.”
Uber says the bulk of the engineering effort behind VerCD has been spent adding company-specific integrations to enable existing systems to interact with ATG’s full end-to-end machine learning workflow. To this end, the latest VerCD’s Orchestrator Service can call various data primitives to build a runtime of a self-driving vehicle for testing, or interact with a code repository while creating images with deep learning libraries and replicating data sets between datacenters and to and from the cloud (should model training occur in these locations).
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