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Complete Model Deployment Project Tutorial

Step-by-step walkthrough of deploying a machine learning model to production.

Project Overview
Step 1: Prepare Model
Step 2: Create FastAPI Service
Step 3: Containerize with Docker
Step 4: Deploy to Cloud
Step 5: Monitor and Test

Project Overview

Project: Deploy ML Model as REST API

Goals: Create a deployable API service with basic health and request handling

Step 1: Prepare Model

import joblib
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

X, y = make_classification(
    n_samples=400, n_features=8, n_informative=4, n_redundant=1, random_state=42
)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train model
model = RandomForestClassifier(n_estimators=50, random_state=42)
model.fit(X_train, y_train)

# Save model
joblib.dump(model, 'model.joblib')
print("Saved model.joblib", "train score", round(model.score(X_test, y_test), 4))

Step 2: Create FastAPI Service

from fastapi import FastAPI
import joblib
import numpy as np

app = FastAPI()
model = joblib.load('model.joblib')

@app.post("/predict")
async def predict(features: list[float]):
    prediction = model.predict([features])[0]
    return {"prediction": int(prediction)}

Step 3: Containerize with Docker

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Step 4: Deploy to Cloud

# Build and push
docker build -t ml-api .
docker push ml-api

# Deploy (platform-specific)

Step 5: Monitor and Test

import requests

resp>'http://localhost:8000/predict', json={'features': [1,2,3,4]})
print(response.json())

Try next: Open Module 14 · MLOps basics and log one training run with metrics.

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