DevOps for Data Scientists: Data Science DevOps

DevOps    |    Intermediate
  • 16 Videos | 1h 19m 26s
  • Includes Assessment
  • Earns a Badge
Likes 9 Likes 9
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 22s
    UP NEXT
  • Playable
    2. 
    Cookiecutter Project Structure
    7m 36s
  • Locked
    3. 
    Using a Cookiecutter Project
    6m 47s
  • Locked
    4. 
    Data Model Life Cycle
    5m 4s
  • Locked
    5. 
    Version Control in Data Science
    3m 56s
  • Locked
    6. 
    Data Model Continuous Integration
    3m 47s
  • Locked
    7. 
    Data and Model Security
    5m 3s
  • Locked
    8. 
    Automated Model Testing
    4m 10s
  • Locked
    9. 
    Data Tools in DevOps
    4m 46s
  • Locked
    10. 
    Monitoring Data Models
    3m 4s
  • Locked
    11. 
    Logging for Data Models
    2m 49s
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    12. 
    Model Performance Measures
    4m 33s
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    13. 
    Data Integration with Cookiecutter
    5m 22s
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    14. 
    Data Integration with Jenkins
    4m 46s
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    15. 
    Data Integration with Travis CI
    4m 24s
  • Locked
    16. 
    Exercise: Implement a Cookiecutter Project
    4m 59s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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