Data Science Workflows using Docker Containers | Future of Data & AI | Data Science Dojo

Want to eliminate the hassle of inconsistent programming environments and achieve reproducible data science workflows? Watch this session to learn how Docker can help you achieve that and more! Learn the basics of Docker, including creating and running containers, working with images, automating image building using Dockerfile, and managing containers on your local machine and in production. Create data science workflows using docker.
You will also see real-world examples of how other data scientists use Docker to streamline their workflow and address common challenges like reproducibility and dependency management. This session is interactive and hands-on so that you can learn and apply the techniques yourself!
About the Speaker:
Sanjay Pant - Senior Data Engineer at Data Science Dojo
Sanjay is a data engineer at Data Science Dojo with experience in cloud infrastructure engineering. He contributes to the design and implementation of scalable cloud solutions for enterprise customers across industries, including proof-of-concept and production-level projects.
Table of Contents:
00:00 - Introduction
02:30 - Evolution of app deployment
05:42 - Why use Docker?
06:55 - Docker Architecture
12:35 - Key concepts
12:55 - Working with Docker images
15:29 - Build images using Dockerfile
18:56 - Manage containers.
21:31 - Data persistence with volumes
23:18 - Manage networks in Docker
26:12 - Manage image storage with Docker registry
28:00 - Multi-container deployment with Docker compose
30:51 - Install Docker Desktop on Mac
31:12 - Install Docker Desktop on Windows
32:05 - Demo
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github link?