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

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.

Introduction
Why use Docker
Docker vs more apps
Build from scratch
Know what youre pulling
docker ignore
nonroot user
sensitive data
best advice
repo to docker
build frequently
code information control
summary
tips
QA