A Small Data Bug Broke the Pipeline — This Is How We Recovered | MLOps EP4

In this MLOps tutorial (EP4), we fix the broken ML model from Episode 3 by building a real, production-grade machine learning pipeline using preprocessing + packaging the correct way.

The model didn’t fail because accuracy was bad — it failed because the preprocessing pipeline and vectorizer were not packaged properly, causing a mismatch between training and inference.

In this episode you will learn:

✔ Why ML models fail in production even with good accuracy
✔ How preprocessing, tokenization, and cleaning impact model stability
✔ How to build a proper scikit-learn Pipeline combining vectorizer + classifier
✔ How to save and load a single unified pipeline.pkl for reproducible inference
✔ How to fix model + vectorizer mismatch issues
✔ How to version this model correctly
✔ How to update the FastAPI inference service using the new pipeline
✔ How to containerize the updated API with Docker
✔ How the new pipeline improves real-world predictions instantly

This is the foundation of production MLOps — stable, reproducible, predictable ML behavior.

🔧 What We Build in EP4

scikit-learn Pipeline object

Combined preprocessing + model logic

Serialized pipeline (pipeline.pkl)

New training script (train_v2)

New inference script (infer_v2)

Updated FastAPI service (api_v2)

Updated Dockerfile

MLflow logging for reproducibility

This episode shows the real difference between:

❌ a “model file”
✔ a production-ready ML pipeline

▶️ Watch Full Episode (EP4)

   • We Deployed a Broken ML Model (FastAPI) — ...  

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