🚀 Build an End-to-End MLOps Pipeline with Airflow, Streamlit, Docker, and Kubernetes! | Tutorial

GitHub Repo: https://github.com/iQuantC/Airflow-Pipeline-Docker-Kubernetes

📌 Description:

Welcome to this beginner-friendly tutorial where we dive into the world of MLOps by building a complete machine learning pipeline! In this video, we walk you through creating an Apache Airflow pipeline to load, train, and evaluate a ML model, save it, and make predictions. Then, we deploy the model with a sleek Streamlit UI, containerize it with Docker, and scale it with Kubernetes. Perfect for anyone starting their MLOps journey! 🎓

🔍 What You'll Learn:

1. How to set up an Airflow DAG to automate loading, training, evaluating, and predicting with a logistic regression model on the Iris dataset.
2. Saving and loading a trained model using joblib.
3. Building an interactive Streamlit app to make predictions with the trained model.
4. Containerizing the Streamlit app with Docker for portability.
5. Deploying the app to Kubernetes using Minikube for scalable production-ready deployment.
6. Troubleshooting common issues like version mismatches, file paths, and container errors.

💻 Tech Stack:

1. Python: For model training and Streamlit app.
2. Apache Airflow: To orchestrate the ML pipeline.
3. Scikit-learn: For logistic regression.
4. Streamlit: For the user interface.
5. Docker: For containerization.
6. Kubernetes (Minikube): For deployment.
7. Joblib & Pandas: For model persistence and data handling.

🎯 Why Watch?

This project is perfect for beginners who want to learn MLOps from scratch! We cover everything from setting up Airflow to deploying a machine learning model in production. Follow along to understand how to automate ML workflows, create user-friendly apps, and use modern deployment tools like Docker and Kubernetes. By the end, you’ll have a fully functional ML pipeline you can showcase in your portfolio! 🚀

Timestamps:

0:00 - Introduction
1:00 - Code Overview
13:00 - Setting up Python Environment
16:15 - Setting up Airflow Environment
24:02 - Training and saving the ML model with Airflow Pipeline
29:47 - Loading the ML model with Streamlit UI
34:08 - Containerizing ML model with Docker
46:28 - Deploying to Kubernetes with Minikube
53:51 - Wrap-up and Clean-up

🔔 Like, Subscribe, and Share!If you found this tutorial helpful, please give it a thumbs up 👍, subscribe for more MLOps and machine learning content, and share it with your friends! Drop your questions or feedback in the comments below—I’d love to hear from you! 💬
#MLOps #Airflow #Streamlit #Docker #Kubernetes #MachineLearning #Python #IrisDataset #DataScience #BeginnerTutorial

Disclaimer: This video is for educational purposes only. The tools and technologies demonstrated are subject to change, and viewers are encouraged to refer to the official documentation for the most up-to-date information.
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GitHub: https://github.com/iQuantC
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Happy MLOpsing! 🎉

Видео 🚀 Build an End-to-End MLOps Pipeline with Airflow, Streamlit, Docker, and Kubernetes! | Tutorial канала iQuant