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Container Technology 2025

Containerized Application for Data Science

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Motivation
A key aspect of a data scientist’s work involves managing and maintaining a reliable development environment. Keeping the development environment up-to-date and closely aligned with the production environment ensures smoother workflows. When these environments diverge, issues can quickly arise. In larger settings, proficiency with CI/CD pipelines and DevOps practices becomes highly beneficial. A data scientist’s primary goal is to deliver solutions that are easy to integrate and deploy into production seamlessly.

Course Summary

Overview Container Technology

Part 1 installation

  • Install Native Python
    Learn how to install and configure a native Python environment on your system, enabling the use of Python directly without additional tools.
  • Install Conda Environment
    A guide to setting up Conda, a versatile environment and package manager, which helps manage dependencies and isolate projects seamlessly.
  • Install Docker Desktop
    Step-by-step instructions for installing Docker Desktop on Windows, providing a containerized environment for consistent and reproducible workflows.

Part 2 Docker Jupyter

This section dives into leveraging Docker for data science, with a focus on JupyterLab and Docker Compose for streamlined workflows.
* Install Docker For Data Science
Instructions for setting up a Docker environment specifically designed for data science tasks, complete with JupyterLab.
* Explain DataScience Dockerfile
An in-depth explanation of the Dockerfile designed for data science, detailing the configurations and tools included to optimize productivity.
* Migrate to Docker compose
Learn how to use Docker Compose to manage multi-container applications, making it easier to handle complex setups for data science projects.