End-to-End MLOps Project | MLflow, Docker, CI/CD & Kubernetes Deployment
In this video, I built and deployed a complete production-ready MLOps pipeline that takes a machine learning model from experimentation to a scalable, containerized API running in a cloud-ready environment.
This project demonstrates how modern ML systems are designed, automated, tested, containerized, and deployed using industry-standard tools.
What This Project Covers
Data preprocessing & feature engineering
Model training with cross-validation
Experiment tracking using MLflow
Reproducible ML pipeline
Automated testing with Pytest
CI/CD automation with GitHub Actions
Docker containerization
FastAPI model serving endpoint
Kubernetes deployment
Logging & monitoring setup
Tech Stack
Python
Scikit-learn
MLflow
FastAPI
Docker
Kubernetes
GitHub Actions
Pytest
How the Environment Was Created
The project was built from scratch using a clean and reproducible setup:
Created a virtual environment
Installed dependencies using requirements.txt
Built a modular training pipeline
Integrated MLflow for experiment tracking
Added unit tests for data and model components
Configured CI/CD to automate linting, testing, and training
Containerized the API using Docker
Deployed using Kubernetes
Enabled logging and monitoring for production readiness
Everything runs from a fresh environment using only the provided requirements file, ensuring full reproducibility.
What This Demonstrates
This project shows how machine learning systems transition from development to real-world production using proper automation, testing, containerization, and deployment workflows.
It reflects how ML is implemented in professional engineering environments.
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