Take machine learning from Jupyter notebook to the cloud: data pipelines, model training with Scikit-Learn, async REST serving with FastAPI, Docker containerization, and AWS EC2 deployment.
A data science project is only valuable when other software systems and users can consume its predictions. This guide outlines the complete production lifecycle: taking a raw transactional dataset, training a robust classifier, packaging it into a low-latency REST API with FastAPI, containerizing it with Docker, and deploying to AWS.
Component 1: Model Serialization. Exporting trained pipelines as joblib or ONNX artifacts.
Component 2: Asynchronous REST Service. Writing a FastAPI microservice with Pydantic payload validation.
Component 3: Docker Containerization. Creating a lightweight Alpine/Slim Python image with non-root security.
Component 4: Cloud Hosting & CI/CD. Pushing images to AWS ECR and hosting on Amazon ECS / EC2 with GitHub Actions.
Clean asynchronous REST API handling real-time prediction queries:
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib
import numpy as np
app = FastAPI(title="Datadrix Production ML API", version="2.0")
pipeline = joblib.load("models/churn_pipeline_v1.joblib")
class CustomerFeatures(BaseModel):
tenure: int
monthly_charges: float
total_charges: float
contract_type: str
@app.post("/api/v1/predict/churn")
async def predict_churn(payload: CustomerFeatures):
data = [[payload.tenure, payload.monthly_charges, payload.total_charges, payload.contract_type]]
prediction = int(pipeline.predict(data)[0])
probability = float(pipeline.predict_proba(data)[0][1])
return {"churn": prediction, "probability": round(probability, 4)}Deploying live model APIs to cloud containers is what differentiates ₹8 LPA applicants from ₹20+ LPA senior hires. Datadrix provides dedicated AWS sandbox environments for student deployment.
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