Vehicle Data → Training Dataset → Model Updates
Automatically curate high-quality training data from autonomous vehicle footage and feed it into machine learning model improvement pipelines.
Workflow Steps
Nomadic
Extract and structure vehicle footage data
Process continuous streams of autonomous vehicle footage through Nomadic's platform. Configure filters to identify edge cases, rare scenarios, and high-value training examples, then export structured annotations and metadata.
Labelbox
Curate and refine training datasets
Import Nomadic's structured data into Labelbox for human review and refinement. Use Labelbox's quality control features to verify annotations, add missing labels, and organize data into training, validation, and test sets.
MLflow
Version and deploy improved models
Connect MLflow to Labelbox to automatically pull curated datasets for model retraining. Track model performance improvements, manage versions, and deploy updated models back to the autonomous vehicle fleet through MLflow's deployment pipeline.
Workflow Flow
Step 1
Nomadic
Extract and structure vehicle footage data
Step 2
Labelbox
Curate and refine training datasets
Step 3
MLflow
Version and deploy improved models
Why This Works
Nomadic automatically identifies valuable training scenarios from massive footage streams, Labelbox ensures data quality, and MLflow manages the entire model improvement lifecycle seamlessly.
Best For
ML engineers need to continuously improve autonomous vehicle models using real-world operational data
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