PRODUCTION ML
Production machine learning at DeepSea Technologies
A multimodal Transformer for vessel anomaly detection, backed by repeatable experiment and release workflows.
The challenge
Run vessel-monitoring inference at production volume while giving a five-engineer team dependable, shared experiment and release practices.
My contribution
Productionized a PyTorch multimodal Transformer with Ray Serve. Led ML platform engineering for a five-engineer team, establishing MLflow experiment tracking, Jenkins CI/CD, blue-green deployment, and over-the-air updates.
The outcome
The model served approximately 20 million predictions a day in a vessel-monitoring system, supported by reusable delivery workflows for the wider ML team.
Areas of work
- Anomaly detection
- Ray Serve
- ML platform