Reproducible Data Science Workflows Using Docker

Learn how to achieve reproducible data science workflows using Docker in this 48-minute video tutorial. Explore Docker basics, including creating and running containers, working with images, automating image building with Dockerfile, and managing containers locally and in production. Discover real-world examples of how data scientists use Docker to streamline workflows and address challenges like reproducibility and dependency management. Gain hands-on experience with interactive demonstrations covering Docker architecture, key concepts, image management, container management, data persistence, networking, image storage, and multi-container deployment. Follow along as the instructor guides you through installing Docker Desktop on Mac and Windows, and participate in a practical demo to reinforce your understanding of Docker's capabilities for data science workflows.

– Introduction
–Evolution of app deployment
– Why use Docker?
– Docker Architecture
– Key concepts
– Working with Docker images
– Build images using Dockerfile
– Manage containers.
– Data persistence with volumes
– Manage networks in Docker
– Manage image storage with Docker registry
– Multi-container deployment with Docker compose
– Install Docker Desktop on Mac
– Install Docker Desktop on Windows
– Demo