Cargo Demand Forecasting on Azure ML: An End-to-End MLOps Pipeline
Forecasting cargo demand is only half the problem; the other half is making that forecast reproducible, deployable, and maintainable in production. This project packages demand forecasting as a full MLOps pipeline rather than a one-off notebook.
Why MLOps, not notebooks
A model that lives in a notebook is hard to reproduce, hard to redeploy, and impossible to trust over time. The goal here was an automated pipeline where every production model can be retrained, versioned, and traced back to the exact data and code that produced it.
The pipeline
Train: models are trained on Azure Machine Learning with tracked experiments.
Register: trained models are versioned in the Azure ML model registry.
Deploy: models are containerized and shipped to Azure Container Instances and Kubernetes for scalable serving.
Monitor: drift monitoring watches for shifts in incoming data and model behavior.
Retrain: traceable retraining workflows regenerate models when drift or new data warrants it.
Technologies used
Azure Machine Learning: experiment tracking, training jobs, model registry, and managed endpoints.
Azure DevOps pipelines: CI/CD automation for training and deployment.
Docker + Azure Container Instances: package and run models as portable containers.
Kubernetes: scalable, production-grade model serving.
Drift monitoring & model registry: reproducibility, versioning, and traceable retraining.
Python: data preparation, feature engineering, and model code.
Impact
The pipeline improved forecasting accuracy and operational efficiency while making the whole system reproducible — the difference between a demo and something a team can rely on.
Tech stack: Azure Machine Learning, Azure DevOps, Docker, Azure Container Instances, Kubernetes, Python, model registry, drift monitoring, CI/CD.
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