🚀 Prototype Machine Learning Model with Streamlit | Deploy with Docker & Kubernetes | Full Tutorial

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

Skills: Supervised Learning53%Docker & Containers53%

About this lesson

Support Us: https://buymeacoffee.com/iquantconsult GitHub Repo: https://github.com/iQuantC/Scikit-learn-Streamlit-Docker-Kubernetes 📌 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! 👍 Like, 🔔 Subscribe, and share if this helped you level up! #MachineLearning #Streamlit #Docker #Kubernetes #MLOps #DataScience #DevOps #ScikitLearn #Minikube #Python Disclaimer: This video is for educ

Original Description

Support Us: https://buymeacoffee.com/iquantconsult GitHub Repo: https://github.com/iQuantC/Scikit-learn-Streamlit-Docker-Kubernetes 📌 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! 👍 Like, 🔔 Subscribe, and share if this helped you level up! #MachineLearning #Streamlit #Docker #Kubernetes #MLOps #DataScience #DevOps #ScikitLearn #Minikube #Python Disclaimer: This video is for educ

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Chapters (9)

Introduction

1:44 Setup Environment

5:11 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

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