Nov 2024/AI/ML/course
Human Stress Prediction Pipeline
Text classification experiments become impossible to reproduce when preprocessing and training live inside isolated notebooks. I built this stress prediction pipeline to prove that operational reviewability matters just as much as the model's accuracy. The goal was to package the entire lifecycle into a consistent and reproducible workflow.
I used TensorFlow Extended to build a structured machine learning pipeline. It handles text vectorization, model training, hyperparameter tuning, and exports the final serving artifacts. The entire execution environment is containerized with Docker to prevent environment drift during deployment.
The biggest takeaway is that deploying machine learning requires strict pipeline boundaries. Treating the model lifecycle and deployment readiness as a core requirement forces a higher standard of engineering discipline. The resulting repository complexity is a fair trade for gaining complete lifecycle clarity.