🚀 How to Prototype Machine Learning Model with Gradio & Docker |For Beginners |Step-by-Step Tutorial

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GitHub Repository: https://github.com/iQuantC/ML_Gradio_App

📌 Description:

In this step-by-step tutorial, learn how to build, visualize, and deploy a Machine Learning model using Gradio for the UI, and Docker for containerization and 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 Gradio App 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

🛠 Technologies Used:

1. Python & Scikit-learn
2. Gradio App for interactive visualization
3. ReportLab for exporting PDF reports
4. Docker for containerization

📌 Chapters

0:00 - Introduction
01:42 - Setup Environment
03:24 - Code & File Review
11:41 - Set up Environment
13:30 - Build ML Model with Scikit-learn in Gradio locally
18:02 - Dockerize the Gradio App w/ Optimized Dockerfile
21:15 - Test App Locally by Running its Docker Container
23:07 - 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 #Gradio #Docker #MLOps #DataScience #DevOps #ScikitLearn #Python #ai


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! 🎉