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1
Introduction
2
Why use Docker
3
Docker vs more apps
4
Build from scratch
5
Know what youre pulling
6
docker ignore
7
nonroot user
8
sensitive data
9
best advice
10
repo to docker
11
build frequently
12
code information control
13
summary
14
tips
15
QA
Description:
Explore best practices for integrating Docker and Python in data science and machine learning workflows through this 46-minute EuroPython Conference talk. Learn how to optimize Docker containers for data-intensive applications, ensure security, and implement efficient deployment strategies. Discover common challenges and solutions when using Docker in scientific computing environments, and gain practical tips to enhance your containerization practices. By the end of the talk, feel confident in adopting Docker across various data science, machine learning, and research projects, with a focus on creating robust, reproducible, and secure containerized environments.

Docker and Python - Making Them Play Nicely and Securely for Data Science and ML

EuroPython Conference
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