Workflow · workflow
Aurora builds centralized ML orchestration layer to accelerate autonomous vehicle Data Engine
Aurora's ML model development workflow was highly manual and fragmented — going from new data to a production model required significant manual effort, parallel experimentation was labor-intensive, there was no unified interface for debugging, and bottlenecks at any stage delayed continuous deployment of new models to vehicles.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Identify data requirements
The Data Engine lifecycle starts with identifying the type of data required to support or improve an AV capability.
Tools used
Kubeflow PipelinesSagemakerEKSTerraformBuildkiteBazelTensorboardBatch APISlackGitHub
Outcome
Aurora drastically cut down the time spent on production and deployment of models on new data, reduced manual effort during experimentation, and sped up model development workflows — the majority of autonomy model developers now use the centralized ML orchestration system.
Results
Time savedwithin two weeks
Source
https://mlops.community/blog/auroras-data-engine-how-we-accelerate-machine-learning-model-workflows
Grounding & classification
Source type: technical build writeup
25 fields verified against source quotes.
builder submittedfailure mode describednamed customerproduction runtime claimedtools describedworkflow describedautomotivecycle time reductionemployee productivitytime savedtechnical build writeupdata sync enrichment