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End-to-End ML Pipeline with Deployment

In short

A notebook with 92% accuracy proves you can train a model. A live URL that anyone can hit, with logs and a retrain plan, proves you can ship one. Recruiters notice the difference.

A complete classical ML project — from raw data to a live, monitored API — demonstrating you can own the full lifecycle, not just the modeling notebook.

The purple blob moves mangoes from a basket along a little conveyor into rows of filled glass jars.

Think of it like this

A small mango pickle factory

Making one jar of pickle in your kitchen is easy. Running a factory means the same washing, cutting, spicing and quality check for every batch, then jars on shop shelves, then listening when customers say this batch tastes off. An end-to-end pipeline is that factory for a model.

Watch it happen

The build plan, stage by stage

Data

Train

Evaluate

Package

Deploy

Monitor

1/6Pull raw data with a script, not by hand. Split train, validation and test once, with a fixed seed.

The big ideas

What you need to know

Reproducible from scratch

Someone should clone your repo, run one or two commands and get the same model. Pin versions and seeds.

One pipeline for train and serve

If preprocessing lives only in your notebook, the API will see different features. Bundle it with the model.

A real API, not a screenshot

Input validation, a health check and a version number in the response. Small things that look professional.

Monitoring is part of the project

Logs, a simple drift check and a retrain plan show you understand models age after launch.

See it in code

Walk through the code

python

1from fastapi import FastAPI2from pydantic import BaseModel3import joblib, pandas as pd4 5app = FastAPI()6model = joblib.load("model.joblib")  # the whole sklearn Pipeline: preprocessing + model7 8class House(BaseModel):9    area_sqft: float10    bedrooms: int11    city: str12 13@app.post("/predict")14def predict(h: House):15    row = pd.DataFrame([h.model_dump()])16    return {"price": float(model.predict(row)[0]), "model_version": "v1"}

1/3Load the trained pipeline once at startup, not on every request.

The serving end of the pipeline. Because the saved file includes preprocessing, the API sends raw fields and gets the exact same treatment as training data.

Save yourself the pain

Common mistakes

Oops

Scaling or encoding features in the notebook, then sending raw values to the deployed model.

Do this instead

Put every transform inside the saved Pipeline. Train and serve must run identical steps.

Oops

Stopping at deployment and calling it end to end.

Do this instead

Add logging and one drift check, even a simple one. That's the part most student projects skip.

Your mission

Milestone one: a live /predict

~45 min

Quick check

Why save the full scikit-learn Pipeline instead of just the fitted model?

Go deeper

1Pick a real problem, source real data, and build data → training → deployment → monitoring.
2Deploy it publicly with a simple frontend or API docs page.
3Write up your modeling decisions and trade-offs in a README.

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