Docker for ML Projects | Build, Run, Push & Pull Containers

Description

In this video, we continue our Docker journey and fully containerize a FastAPI-based machine learning API. This is Part 2 of our End-to-End Data Science Project series, where we cover: - Building a Docker image from Dockerfile - Running Docker containers - Understanding port mapping - Pushing Docker images to Docker Hub - Pulling images from Docker Hub - Running the FastAPI ML API from Docker - Testing the API using browser and Swagger UI - Common Docker mistakes and fixes By the end of this video, you will have a complete Dockerized ML API that works locally and is ready for cloud deployment. 📌 Playlist: End-to-End Data Science Projects 📌 Tech stack: Docker, FastAPI, Python, Machine Learning In the next videos, we will deploy this Docker container on the cloud and connect it with a frontend.