Model deployment
Model deployment connects data collected by the fleet to models running on edge hardware. The pipeline checks incoming data, orchestrates training and evaluation, converts models for their target platforms, and introduces updates progressively. Health monitoring and rollback remain part of the deployment process as coverage expands.
How it connects
- Ingest fleet data
- Validate quality
- Train and evaluate
- Optimize for hardware
- Roll out progressively
- Monitor and roll back
Check the data before training
Fleet data ingestion combines collection with quality validation. Blur detection, exposure analysis, and corruption scanning assess the incoming material before the training stage. These checks make data quality an explicit part of the pipeline, connecting the observations collected during operations to the preprocessing, training, and evaluation work that follows them.
Prepare models for their target hardware
The training stage orchestrates preprocessing, model training on GPU clusters, and evaluation against performance thresholds. Multi-platform optimization then converts and prepares the model for each target edge hardware platform. This connects the training workflow to the hardware that will run the model, before the fleet deployment stage begins distributing the update.
Expand deployment progressively
A canary rollout introduces an update to a limited portion of the fleet before expanding deployment more broadly. Health monitoring accompanies that progression, with automatic rollback when failures are detected. The pipeline therefore includes both the path for distributing a new model and the response when an update does not remain healthy in operation.