How to Deploy ML Solutions with FastAPI, Docker, & AWS

🤝 Your team not maximizing Claude? I run 1:1 and team AI workshops for companies doing $10M+ per year: https://aibuilder.academy/yt/pJ_nCklQ65w This is the 5th video in a series on Full Stack Data Science. Here, I walk through a simple 3-step approach for deploying machine learning solutions. More Resources: 💻 Example Code: https://github.com/ShawhinT/YouTube-Blog/tree/main/full-stack-data-science/ml-engineering 📰 Read more: https://medium.com/towards-data-science/how-to-deploy-ml-solutions-with-fastapi-docker-and-gcp-de1bb8bfc59a?sk=5048e53b3599a37a126379514328e2e3 🛠️ Previous Video: https://youtu.be/sNa_uiqSlJo ➡️ Data Pipeline Video: https://youtu.be/Ylz779Op9Pw References: [1] FastAPI Tutorial: https://fastapi.tiangolo.com/tutorial/first-steps/ [2] FastAPI + Docker: https://fastapi.tiangolo.com/deployment/docker/ [3] Deploying on AWS ECS: https://www.youtube.com/watch?v=1H83IRK4RXw Intro - 0:00 ML Deployment - 0:33 3-Step Deployment Approach - 1:52 Example Code: Deploying Semantic Search for YT Videos - 3:21 Creating API with FastAPI - 4:31 Create Docker Image - 11:13 Push Image to Docker Hub - 17:15 Deploy Container on AWS ECS - 19:46 Testing Gradio UI - 25:54 What's Next? - 27:07