Course details

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

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.



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