PRODUCTION ML
Production machine learning at DeepSea Technologies
Anomaly detection, missing-data modeling, and weather-aware vessel routing across sparse, irregular, and corrupted time series.
The challenge
Extract reliable operational signals from missing observations, inconsistent sampling, corrupted sensor data, and changing weather conditions.
My contribution
Led a five-engineer ML platform team building anomaly detection, vessel-performance models, predictive maintenance, and weather-aware routing. Worked on missing-data imputation and productionized a multimodal Transformer with monitoring and over-the-air updates.
The outcome
20M predictions daily; 50% less bunker-fuel fraud across 1,000 vessels; routing for 100+ vessels. Additional models reduced fuel consumption by 4% and maintenance costs by 10%.
Related research: MAIN — missing-data imputation ↗
Areas of work
- Anomaly detection
- Sparse time series
- Weather-aware routing