Previous Page

DevOps for Data Scientists: Containers for Data Science

DevOps for Data Scientists: Containers for Data Science


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore the use of containers in deploying data science solutions using Docker with R, Python, Jupyter, and Anaconda.



Expected Duration (hours)
1.0

Lesson Objectives

DevOps for Data Scientists: Containers for Data Science

  • discover the key concepts covered in this course
  • describe the use of containers for data science
  • describe approaches to infrastructure as code for data deployment
  • describe Ansible and Vagrant approaches to data science deployment
  • describe provisioning tools used in data science
  • use Docker to build a data model
  • use Docker to perform model testing for deployment
  • use Docker to manage R deployments
  • use Docker for a PostgreSQL deployment
  • create a Docker persistent volume
  • use Jupyter Docker Stacks to get up and running with Jupyter
  • use the Anaconda distribution to run a Jupyter Notebook
  • use Jupyter Notebooks with a Cookiecutter data science project
  • use Docker Compose with PostgreSQL and Jupyter Notebooks
  • use a container deployment for Jupyter Notebooks with R
  • use a container strategy for a Jupyter deployment
  • Course Number:
    it_dsdods_04_enus

    Expertise Level
    Intermediate