🚀 Prototype Machine Learning Model with Streamlit | Deploy with Docker & Kubernetes | Full Tutorial
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GitHub Repo: https://github.com/iQuantC/Scikit-lea...
📌 Description:
In this step-by-step tutorial, learn how to build, visualize, and deploy a Scikit-learn machine learning model using Streamlit for the UI, Docker for containerization, and Kubernetes (Minikube) for scalable deployment! This project is perfect for data scientists, ML engineers, and DevOps beginners who want to bring machine learning models to life in a real-world environment.
🔍 What You’ll Learn:
1. How to build a simple ML classification model using Scikit-learn
2. How to create an interactive Streamlit UI to explore results
3. How to generate PDF reports with charts and performance metrics
4. How to containerize the app using Docker
5. How to optimize Dockerfile for smaller image size
6. How to deploy and expose the app on a Kubernetes cluster with Minikube
🛠 Technologies Used:
1. Python & Scikit-learn
2. Streamlit for interactive visualization
3. ReportLab for exporting PDF reports
4. Docker for containerization
5. Kubernetes (Minikube) for orchestration
🚢 Deployment Stack:
1. Optimized Docker Image (DockerHub-ready)
2. Kubernetes Deployment + Service YAML
3. Local access via NodePort in Minikube
📌 Chapters
0:00 - Introduction
01:44 - Setup Environment
05:11 - Build ML Model with Scikit-learn in Streamlit UI locally
11:42 - Dockerize the Streamlit App w/ Optimized Dockerfile
17:40 - Test App Locally by Running its Docker Container
19:40 - Tag & Push Docker Image to DockerHub
21:47 - Create Minikube Cluster
22:57 - Deploy ML App to Kubernetes
28:07 - Final Wrap-up
💬 Let me know in the comments if you want to see this deployed to Google Cloud Run, AWS ECS, or integrated with CI/CD pipelines!
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#MachineLearning #Streamlit #Docker #Kubernetes #MLOps #DataScience #DevOps #ScikitLearn #Minikube #Python
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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Happy MLOpsing! 🎉