Learning MLOps by Building a Real-World Salary Prediction Pipeline (MLflow + FastAPI + Docker)

🚀 Hi , I am Going to Build a Real-World Salary Prediction MLOps Pipeline with MLflow

Learn experiment tracking, model versioning, and deployment by building an ML pipeline that predicts employee salaries.

This project is the foundation of my 15-day MLOps learning sprint — and I’m building it in public on LinkedIn & GitHub!

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👋 Why I Built This

To Learn Mlops Pratically So I decided to learn by building a real, end-to-end project that involves:

• ✅ Tracking models & metrics with MLflow
• ✅ Serving predictions via FastAPI
• ✅ Adding a UI using Streamlit
• ✅ Preparing for production with Docker & CI/CD
• ✅ Building in public to grow with the community

I chose a practical use case — predicting employee salaries — and turned it into a full MLOps pipeline that simulates real-world AI workflows.

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This project kicks off my 15-day hands-on MLOps learning challenge, where I’ll:

• Build and track experiments with MLflow
• Serve the model via an API
• Add a clean UI for users
• Package and deploy the app to the cloud
• Learn CI/CD automation for model updates

💼 LinkedIn

If you're learning MLOps too — join me! Let’s grow together.