Lightweight Cancer Prediction Service With a Pre-Trained SVM

Added Jun 2025 3 design docs

Hospitals and health-tech products consume predictions through APIs, not notebooks, and a well-built inference service is often the first thing a junior machine learning engineer is asked to deliver. This project is that deliverable in miniature: a clean prediction backend for a cancer detection model.

The intern takes a pre-trained Support Vector Machine classifier built with scikit-learn and wraps it in a FastAPI service. The work centers on serving concerns rather than modeling: loading the serialized model once at startup, defining typed request schemas for uploaded diagnostic data, validating inputs before they ever reach the model, and returning structured predictions with appropriate error responses when data is malformed. FastAPI's automatic interactive documentation lets anyone exercise the endpoint from a browser, and the intern tests the service against realistic payloads to confirm it behaves predictably.

By scoping out model training, the project isolates and strengthens the serving skill set — API design, validation, serialization handling, and documentation — giving the intern a precise understanding of the boundary between data science and backend engineering, and a working healthcare inference service for their portfolio.

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