DevOps for Data Scientists: Data Science DevOps

DevOps
  • 16 Videos | 1h 13m 14s
  • Includes Assessment
  • Earns a Badge
Likes 8
In this 16-video course, learners discover the steps involved in applying DevOps to data science, including integration, packings, deployment, monitoring, and logging. You will begin by learning how to install a Cookiecutter project for data science, then look at its structure, and discover how to modify a Cookiecutter project to train and test a model. Examine the steps in the data model lifecycle and the benefits of version control for data science. Explore the tools and approaches to continuous integration for data models, to data and model security for Data DevOps, and the approaches to automated model testing for Data DevOps. Learn about the Data DevOps considerations for data science tools and IDEs (integrated developer environment) and the approaches to monitoring data models and logging for data models. You will examine ways to measure model performance in production and look at data integration with Cookiecutter. Then learn how to implement a data integration task with both Jenkins and Travis CI (continuous integration). The concluding exercise involves implementing a Cookiecutter project.

WHAT YOU WILL LEARN

  • discover the subject areas covered in this course
    examine a Cookiecutter project structure
    modify a Cookiecutter project to train and test a model
    describe the steps in the data model life cycle
    describe the benefits of version control for data science
    describe tools and approaches to continuous integration for data models
    describe approaches to data and model security for Data DevOps
    describe approaches to automated model testing for Data DevOps
  • identify Data DevOps considerations for data science tools and IDEs
    identify approaches to monitoring data models
    describe approaches to logging for data models
    identify ways to measure model performance in production
    add directives to the make file to prepare for continuous integration
    implement a data integration task with Jenkins
    implement data integration with Travis CI
    incorporate a model into a Cookiecutter project

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 25s
    UP NEXT
  • Playable
    2. 
    Cookiecutter Project Structure
    7m 39s
  • Locked
    3. 
    Using a Cookiecutter Project
    6m 50s
  • Locked
    4. 
    Data Model Life Cycle
    5m 7s
  • Locked
    5. 
    Version Control in Data Science
    3m 59s
  • Locked
    6. 
    Data Model Continuous Integration
    3m 50s
  • Locked
    7. 
    Data and Model Security
    5m 6s
  • Locked
    8. 
    Automated Model Testing
    4m 13s
  • Locked
    9. 
    Data Tools in DevOps
    4m 49s
  • Locked
    10. 
    Monitoring Data Models
    3m 6s
  • Locked
    11. 
    Logging for Data Models
    2m 52s
  • Locked
    12. 
    Model Performance Measures
    4m 36s
  • Locked
    13. 
    Data Integration with Cookiecutter
    5m 25s
  • Locked
    14. 
    Data Integration with Jenkins
    4m 49s
  • Locked
    15. 
    Data Integration with Travis CI
    4m 27s
  • Locked
    16. 
    Exercise: Implement a Cookiecutter Project
    5m 2s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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