Context

Accurate demand forecasting is critical to optimizing inventory management and reducing stockout risks. This project implements a time-series forecasting model using LSTM networks to predict future demand and crude oil price trends, wrapped in a production-grade MLOps pipeline.

Approach

Built using TensorFlow/Keras for LSTM training and evaluation. Served the inference model via a RESTful FastAPI backend. The project highlights MLOps best practices with over 95% test coverage, comprehensive unit testing (for preprocessing, training, inference, and endpoints), and a fully automated CI/CD pipeline built with GitHub Actions that runs tests and packages the system inside Docker containers on every release.

Outcome

95% Test Coverage.

Reflection

Shipping a model is only 10% of the work. Automating model testing, data drift checks, and containerized deployments taught me how to construct stable CI/CD pipelines that ensure machine learning models perform reliably in production.