Machine learning · Production data pipelines
Material Usage Prediction
Production ETL and ML model exposure for inferential materials planning, monitoring, and scaling.
The challenge
Materials planning benefits from predictions only when the underlying data and model outputs can be delivered reliably. Moving from a useful model to an operational planning capability requires repeatable data preparation, model packaging, and production monitoring.
The approach
The project combines production ETL with an exposed machine-learning model for inferential material-usage planning. PostgreSQL supports the data workflow, Docker supplies a consistent runtime, and MLflow helps manage the model lifecycle.
The delivery work covers:
- preparing model inputs through production ETL;
- packaging model execution in a reproducible environment;
- exposing predictions to downstream planning workflows; and
- establishing a foundation for monitoring and scaling.
Engineering focus
The system treats the data pipeline and model runtime as one production capability. Reproducible packaging and model lifecycle controls reduce the gap between experimentation and operational use, while the database-backed workflow keeps inputs and outputs available to the wider planning process.