Build & Deploy an ML App with FastAPI, Streamlit, Docker & GitHub Actions 🚀!

Description

Build & Deploy an ML App with FastAPI, Streamlit, Docker & GitHub Actions 🚀 Learn how to build and deploy a complete Machine Learning application using FastAPI, Streamlit, Pytest, Docker, and GitHub Actions CI/CD pipelines step-by-step. In this video, we create a scalable end-to-end ML workflow that covers everything from model training and testing to containerization and automated deployment. This project is perfect for: Data Scientists ML Engineers MLOps Engineers Python Developers AI Beginners wanting real-world deployment skills 🚀 What You’ll Learn: Build a Machine Learning model Create APIs with FastAPI Build a frontend using Streamlit Write automated tests with Pytest Containerize applications with Docker Automate CI/CD using GitHub Actions Deploy scalable ML workflows Structure production-ready ML projects 🧠 Technologies Used: Python Scikit-learn FastAPI Streamlit Pytest Docker GitHub Actions CI/CD Pipelines Machine Learning MLOps 📊 Recommended Dataset: heart.csv Heart Attack Prediction Dataset (Kaggle) This tutorial demonstrates how modern AI applications are built and deployed in real production environments using automation, testing, and scalable infrastructure. 🔥 Perfect for portfolio projects, interview preparation, and real-world ML engineering experience. 👍 Don’t forget to LIKE, SUBSCRIBE, and SHARE if you enjoy AI, Data Science, Machine Learning, and MLOps content. #MachineLearning #MLOps #FastAPI #Docker #GitHubActions #Pytest #Streamlit #CICD #Python #AIEngineering #MLDeployment #DataScience #ArtificialIntelligence #DevOps #MLProject