Docker for ML Projects | Basics & Dockerfile

Description

In this video, we start learning Docker from scratch and understand how it is used in real-world machine learning projects. This is Part 1 of our End-to-End Data Science Project series, where we cover: - What Docker is and why it is needed - Problems Docker solves in ML deployment - Docker concepts: Image, Container, Dockerfile - How Docker works for FastAPI ML APIs - Writing a Dockerfile step by step - Understanding each Dockerfile instruction clearly This video is beginner-friendly and focuses on building strong fundamentals before running or deploying containers. In Part 2, we will: - Build the Docker image - Run the container - Push and pull images from Docker Hub - Test the FastAPI ML API running inside Docker 📌 Playlist: End-to-End Data Science Projects 📌 Tech stack: Python, FastAPI, Docker, Machine Learning This video is ideal for data science and ML learners who want to understand deployment the right way.