a real-world example where MLOps improved model deployment and scalability.

Create a GitHub repository for a sample ML project.

Set up a GitHub Actions workflow to test and lint an ML project.

Convert a sample Jupyter Notebook to a Python script.

Build and deploy a REST API using Flask.

Extend it using FastAPI for better performance

Create a Streamlit app to interact with an ML model.

Package and publish a sample ML project to TestPyPI.

Track a simple ML experiment using MLflow.

Build an end-to-end MLOps pipeline for a classification problem.