DHL DATA & AI ENGINEERING
Machine learning platform for predictive logistics and supply chain optimisation.
2025
AI Engineering
Active
DHL needed to move beyond reactive logistics management towards predictive operations. NexOps built a scalable ML platform on Azure that enables DHL's data science teams to develop, deploy, and monitor machine learning models for demand forecasting, route optimisation, and anomaly detection across their European distribution network.
METHODOLOGY
- 01
Architected an MLOps pipeline on Azure Machine Learning with Databricks Feature Store for feature engineering at scale
- 02
Built CI/CD pipelines for model training, validation, and deployment using Azure DevOps and MLflow
- 03
Implemented real-time inference endpoints on Azure Kubernetes Service with auto-scaling
- 04
Designed a monitoring framework for model drift detection and automated retraining triggers
OUTCOMES
- →
Deployed 12 production ML models serving predictions for 500K+ daily shipments
- →
Demand forecasting accuracy improved by 23%, reducing overstock and understock events
- →
Model deployment cycle reduced from 6 weeks to 3 days through automated MLOps pipelines
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