DevOps for Data Scientists: Containers for Data Science

DevOps    |    Intermediate
  • 16 Videos | 1h 5m 13s
  • Includes Assessment
  • Earns a Badge
Likes 15 Likes 15
In this 16-video course, explore the use of containers in deploying data science solutions by using Docker with R, Python, Jupyter, and Anaconda. Begin with an introduction to containers and their use for deployment and data science. Then examine approaches to infrastructure as code for data deployment, and concepts behind Ansible and Vagrant approaches to data science deployment. Explore the main features of provisioning tools used in data science. You will learn how to use Docker to build data models, then use it to perform model testing for deployment, to manage R deployments, and for a PostgreSQL deployment. Also, discover how to use Docker for persistent volumes. Next, learners look at using Jupyter Docker Stacks to get up and running with Jupyter and using the Anaconda Distribution to run a Jupyter Notebook. This leads into using Jupyter Notebooks with a Cookiecutter data science project. Then learn about using Docker Compose with PostgreSQL and Jupyter Notebook, and using a container deployment for Jupyter Notebooks with R. The concluding exercise involves deploying Jupyter.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 22s
    UP NEXT
  • Playable
    2. 
    Introduction to Containers for Data Science
    4m 37s
  • Locked
    3. 
    Infrastructure as Code for Data
    3m 14s
  • Locked
    4. 
    Ansible and Vagrant for Data
    3m 49s
  • Locked
    5. 
    Provisioning for Data Science
    2m 58s
  • Locked
    6. 
    Using Docker for Data Science
    5m 3s
  • Locked
    7. 
    Deploying Docker Images for Data Science
    3m 11s
  • Locked
    8. 
    Docker for R
    4m 24s
  • Locked
    9. 
    Docker for SQL
    3m 21s
  • Locked
    10. 
    Docker Persistent Volumes
    4m 21s
  • Locked
    11. 
    Using Jupyter Docker Stacks
    2m 37s
  • Locked
    12. 
    Anaconda Packages for Jupyter
    3m 16s
  • Locked
    13. 
    Cookiecutter Data Science with Jupyter
    2m 44s
  • Locked
    14. 
    Docker Compose for Data Science
    5m 20s
  • Locked
    15. 
    Using R in Jupyter with Docker
    4m 20s
  • Locked
    16. 
    Exercise: Deploying Jupyter
    3m 36s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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