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DevOps for Data Scientists: Data Science DevOps

DevOps for Data Scientists: Data Science DevOps


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Discover the steps in applying DevOps to data science, including integration, packings, deployment, monitoring, and logging.



Expected Duration (hours)
1.2

Lesson Objectives

DevOps for Data Scientists: Data Science DevOps

  • 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
  • Course Number:
    it_dsdods_02_enus

    Expertise Level
    Intermediate