🧠 AI with Python – 🌍 Deploy FastAPI ML App on Render / Railway

Once a machine learning model works locally, the real test begins when you make it accessible to the outside world.

Deployment turns your ML code into a real service—something other applications, users, or systems can interact with over the internet.

In this project, we deploy a FastAPI-based ML inference app to cloud platforms like Render or Railway, transforming a local ML API into a publicly accessible endpoint.

Understanding the Problem

Local ML APIs are limited:

• only accessible on your machine
• unavailable to other applications
• unsuitable for real users or integrations

To use ML in real products, the model must be deployed to a cloud environment where it:

• runs continuously
• exposes a public URL
• scales automatically
• requires minimal infrastructure management

Platforms like Render and Railway simplify this process by handling servers, networking, and runtime setup for you.

1. Prepare the FastAPI ML App

Before deployment, the FastAPI app should already work locally.

from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import numpy as np

model = joblib.load("iris_model.joblib")

app = FastAPI(title="Deployed ML API")

class InputData(BaseModel):
    features: list

@app.post("/predict")
def predict(data: InputData):
    arr = np.array(data.features).reshape(1, -1)
    return {"prediction": model.predict(arr).tolist()}

If this API runs locally, it’s ready for cloud deployment.

2. Install Required Packages

Ensure all dependencies are explicitly installed.

pip install fastapi uvicorn scikit-learn joblib numpy

Cloud platforms will use this same setup during deployment.

3. Deploying on Render

Render provides a simple way to deploy web services directly from GitHub.

High-level steps:

• Push your FastAPI code and model file to GitHub
• Create a new Web Service on Render
• Set the start command to run Uvicorn

uvicorn main:app --host 0.0.0.0 --port $PORT

Render automatically assigns a port and exposes a public URL for your API.

4. Deploying on Railway

Railway offers a similar experience with minimal configuration.

High-level steps:

• Connect your GitHub repository
• Select the FastAPI project
• Use the same Uvicorn start command

uvicorn main:app --host 0.0.0.0 --port $PORT

Railway handles environment setup and service restarts automatically.

5. Testing the Deployed ML API

Once deployed, the API can be tested from anywhere using its public URL.

curl -X POST "https://your-app-url/predict" \
     -H "Content-Type: application/json" \
     -d '{"features":[5.8,2.7,5.1,1.9]}'

A valid prediction confirms the ML model is live and accessible.

Why This Deployment Step Is Important

Deploying to Render or Railway enables:

• real-world usage of ML models
• integration with frontend apps and services
• easier collaboration and demos
• scalable, always-on inference

This is where ML moves from experimentation to production-ready systems.

Key Takeaways

1. Cloud deployment makes ML models accessible via public APIs.
2. FastAPI apps can be deployed without major code changes.
3. Render and Railway remove the need for server management.
4. Public endpoints enable real-world ML integration.
5. This step completes the transition from local ML to cloud ML.

Conclusion

Deploying an ML API to the cloud is a defining milestone in the machine learning workflow.

By hosting your FastAPI app on platforms like Render or Railway, your model becomes a real service—ready to power applications, experiments, and products.

With this step completed, your AI with Python journey has moved firmly into real-world ML deployment, setting the stage for monitoring, scaling, and production hardening.