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

iQuant · Beginner ·☁️ DevOps & Cloud ·1y ago

Skills: Supervised Learning53%Docker & Containers53%

About this lesson

Support Us: https://buymeacoffee.com/iquantconsult 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! 👍 Like, 🔔 Subscribe, and share if this helped you level up! #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. Follow Us: GitHub: https://github.com/iQuantC Instagram: https://www.instagram.com/iquantconsult/ Happy MLOpsing! 🎉

Original Description

Support Us: https://buymeacoffee.com/iquantconsult 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! 👍 Like, 🔔 Subscribe, and share if this helped you level up! #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. Follow Us: GitHub: https://github.com/iQuantC Instagram: https://www.instagram.com/iquantconsult/ Happy MLOpsing! 🎉

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

More on: Supervised Learning

View skill →

Auto Machine Learning (AutoML) Using AutoGluon

Auto Machine Learning (AutoML) Using AutoGluon

Coding the SARIMA Model : Time Series Talk

Coding the SARIMA Model : Time Series Talk

Code With Me : Logistic Regression (from scratch) !

Code With Me : Logistic Regression (from scratch) !

Predicting the Winning Team with Machine Learning

Predicting the Winning Team with Machine Learning

Air Quality Index Prediction in Python | Machine Learning Projects | GeeksforGeeks

Air Quality Index Prediction in Python | Machine Learning Projects | GeeksforGeeks

What is K-Nearest Neighbors?

What is K-Nearest Neighbors?

Related Reads

AWS Self-Healing Infrastructure: Catching and Fixing Server Crashes with AI Autopilot

Learn how to use AWS Self-Healing Infrastructure with AI Autopilot to catch and fix server crashes automatically

Medium · DevOps

Understanding a Real AWS CodePipeline CI/CD Flow: Source Build Deploy"

Learn how to set up a CI/CD pipeline using AWS CodePipeline, covering source, build, and deploy stages

Dev.to · Guna SantoshDeep Srivastava

How to Use Terraform with Python for Infrastructure

Learn to manage infrastructure with Terraform and Python for efficient and automated deployment

Interactive containers and exec: get inside, run commands, get out

Learn to interact with Docker containers using exec, cp, and shells to run commands and manage files

Dev.to · Javi Palacios

Chapters (8)

Introduction

1:42 Setup Environment

3:24 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

AWS, Azure, GCP: The One Thing Every Business Gets Wrong