Going Live: How I Deployed My Water Quality ML API to the Cloud in 5 Minutes

"It works on my machine."

Every developer knows this phrase. I had just finished building a Water Quality Prediction system using Python, Flask, and Docker. It was running perfectly on my laptop. I could send a curl request to localhost:5000 and get a prediction back instantly.

But "localhost" doesn't help the world. If I wanted this model to actually be useful—perhaps as a backend for a mobile app or an IoT river monitoring system—I needed a public URL.

In this post, I’ll walk through the final (and surprisingly easiest) step of my project: deploying a Dockerized Flask app to the cloud using Render.

The Setup: What We Are Deploying

Before we jump into the cloud, here is a quick snapshot of what I built:

• The Brain: A Random Forest Classifier trained to predict water quality classes (0, 1, 2).
• The Body: A Flask API that accepts JSON data (pH, Turbidity, etc.) and returns predictions.
• The Container: A Dockerfile that packages everything into a portable unit.

Because I had already done the hard work of Dockerizing the app (fixing file extension errors and network timeouts!), moving to the cloud was straightforward.

Why I Chose Render?

I looked at a few options like Vercel and Heroku.

• Vercel is great for frontends, but it’s "serverless," which means it puts Python apps to sleep aggressively. This causes "cold starts" where the model takes 10+ seconds to load into memory on the first request.
• Render supports Docker natively. It basically says, "Give me your Dockerfile, and I will run it exactly like you did on your laptop." Plus, it has a generous free tier.

Step 1: Git Push

The first step was getting my code off my laptop. I created a new repository on GitHub and pushed my code.

git init
git add .
git commit -m "Ready for deployment"
git remote add origin [https://github.com/my-username/water-quality-api.git](https://github.com/my-username/water-quality-api.git)
git push -u origin main

`

Crucial Tip: I made sure my .gitignore file prevented me from uploading unnecessary junk like __pycache__ or virtual environments.

Step 2: Connecting to Render

This process was refreshingly simple:

1. I logged into the Render Dashboard.
2. Clicked New + -> Web Service.
3. Connected my GitHub account and selected the repository.

Here is where the magic happened. Render automatically detected my Dockerfile. I didn't have to configure Python versions or install system dependencies manually. I simply selected the Free plan and hit Create Web Service.

Step 3: The Build (Cloud Edition)

Watching the build logs on a remote server is a satisfying experience. I saw the exact same steps scrolling by that I saw in my local terminal:

• Installing numpy...
• Installing pandas...
• Exporting to image...

After about 5 minutes, the logs went green: Your service is live.

The Moment of Truth

Render gave me a shiny new URL: https://catfish-water-quality-prediction.onrender.com.

It was time to test it. I opened my terminal and sent a request—not to localhost, but to the real internet.

bash
curl -X POST [https://catfish-water-quality-prediction.onrender.com/predict](https://catfish-water-quality-prediction.onrender.com/predict) \
-H "Content-Type: application/json" \
-d '{
"Temp": 67.4,
"Turbidity (cm)": 10.1,
"DO(mg/L)": 0.2,
"pH": 4.7,
"Plankton (No. L-1)": 6069.6,
"BOD (mg/L)": 7.4,
"CO2": 10.1,
"Alkalinity (mg L-1 )": 218.3,
"Hardness (mg L-1 )": 300.1,
"Calcium (mg L-1 )": 337.1,
"Ammonia (mg L-1 )": 0.2,
"Nitrite (mg L-1 )": 4.3,
"Phosphorus (mg L-1 )": 0.005,
"H2S (mg L-1 )": 0.06
}'


The Result:

json
{
"water_quality_prediction": 2
}


Success! My model is now accessible from anywhere in the world.

Conclusion

Taking a Machine Learning model from a Jupyter Notebook to a live API feels like a superpower.

The combination of Docker + Render is incredibly powerful for Data Scientists. Docker ensures the environment is consistent, and Render takes care of the infrastructure. If you have a model sitting on your hard drive gathering dust, I highly recommend trying this workflow to set it free.




Comments

[Diego Martinez]

This feels like applying the “Twelve-Factor App” philosophy in practice: you've cleanly separated code, config, and environment with Docker, then let Render handle the stateless deploy so your ML model moves from laptop artifact to real, web-ready service.