MLOps Project Ideas to Get You Hired

The truth, as I learned the hard way, is that a model sitting in a Jupyter notebook is nothing but a practice exercise. It hasn’t delivered any value yet. That is where MLOps (MachineLearning Operations) comes in. MLOps is the engineering discipline that makes our AI reliable, scalable, and useful. For students, this is the single biggest gap in most portfolios. If you want to get hired, proving you can build this bridge is everything. Here are three project ideas based on MLOps that will make your portfolio stand out.

Deploy an ML Model using Docker

In this project, you’re going to take a trained model and package it as a portable, self-contained application.

Here’s how to work on this project:

1. Train a Machine Learning model.
2. Save the trained model as a file (e.g., model.pkl).
3. Build a simple API with FastAPI that loads this model and has one endpoint that takes new text and returns a prediction.
4. Write a Dockerfile that copies your code, installs the dependencies from a requirements.txt file, and runs the FastAPI server.
5. Build and run your container locally. You now have a real ML application!

Here’s a guided example to deploy an ML model using Docker.

Orchestrate a Pipeline with Model Monitoring

In this project, you will develop a system that automatically checks whether your live model is still performing well and re-trains it if necessary.

Here’s how to work on this project:

1. Use a dataset that changes over time.
2. Set up a free tool like Evidently AI to compare a reference dataset (your training data) with a current dataset (new data).
3. Generate an HTML dashboard from Evidently AI that visualizes the drift.
4. Write a Python script (your orchestrator) that runs this check daily. If drift is detected, have it print “Drift Detected! Re-training required.”

Here’s a guided example to orchestrate a pipeline with model monitoring.

Create a Continuous Delivery (CD) Pipeline

In this project, you’re going to combine the first two projects into an automated assembly line that tests and deploys your model every time you make a change.

Think of it as this automated factory:

1. CI (Integration): You git push new code (you try a new feature). GitHub Actions automatically wakes up, runs your code tests (“does the API still load?”), and validates your model.
2. CD (Delivery): If all tests pass, the pipeline automatically builds your Docker container (from Project 1) and pushes it to a container registry. It could even automatically deploy it to a small cloud server.
3. CT (Training): Your pipeline from Project 2 can also be a part of this, running on a schedule (weekly) to re-train the model.

Here’s a guided example to create a continuous delivery pipeline.

Final Words

These projects might feel a little different, less like pure data science and more like engineering. That’s okay. In fact, that’s the entire point. The most brilliant, complex neural network in the world is useless if it’s trapped in a notebook. Building these pipelines is the craft that unleashes that potential, safely and reliably, for everyone to use.

I hope you liked this article on project ideas based on MLOps that will make your portfolio stand out. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.