Mar 21
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In this video you'll deploy a real machine learning model using Docker — step by step from scratch. No prior Docker experience needed. We train a RandomForest classifier using scikit-learn, wrap it in a FastAPI REST endpoint, containerize it with Docker, and test it live. By the end you'll have a fully working ML model running inside a container — returning predictions via API. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⏱️ CHAPTERS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0:00 — Introduction 0:45 — What you'll build today 1:45 — Project structure (5 files, 5 jobs) 3:15 — Train and save the ML model (model.py) 5:30 — Build the FastAPI server (app.py) 8:30 — Write the Dockerfile — line by line 11:00 — Docker build and run 12:30 — Test the live API 13:00 — Outro + what's next ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🛠️ TOOLS USED ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Python 3.11 • scikit-learn (RandomForestClassifier) • FastAPI + uvicorn • Docker Desktop • VS Code ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💻 CODE — Copy these files ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ model.py — trains RandomForest on Iris dataset and saves to model.pkl app.py — FastAPI server with /predict endpoint returning flower name Dockerfile — containerizes the full project into a portable image requirements.txt — all dependencies pinned for reproducibility Test your API locally: 👉 http://localhost:8000/predict?f1=5.1&f2=3.5&f3=1.4&f4=0.2 Expected response: {"prediction": 0, "flower": "Iris Setosa"} ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📺 WATCH THE FULL SERIES ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Episode 01 — What is MLOps? Complete Beginner Guide → [ADD LINK] Episode 02 — Deploy ML Model with Docker (this video) Episode 03 — Push to AWS Cloud → Coming soon ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔑 WHO IS THIS FOR? ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✅ Python developers moving into ML engineering ✅ Data scientists who want to deploy their models ✅ DevOps engineers adding ML to their skillset ✅ Anyone who wants to learn MLOps from scratch ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📌 WHAT YOU'LL LEARN ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✔ How to train and save an ML model using scikit-learn ✔ How to serve predictions via a REST API with FastAPI ✔ How to write a production-grade Dockerfile from scratch ✔ How to build and run a Docker container locally ✔ How to test a live ML API in your browser ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔔 SUBSCRIBE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Subscribe for practical AI + DevOps tutorials — no fluff, just real skills. New video every week. 🚀 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ #MLOps #Docker #MachineLearning #FastAPI #Python #DevOps #AIDevOps #MLDeployment #scikitlearn #DockerTutorial