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DevOps for Data Scientists: Data DevOps Concepts

DevOps for Data Scientists: Data DevOps Concepts


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

To carry out DevOps for data science, you need to extend the ideas of DevOps to be compatible with the processes of data science and machine learning. Explore the concepts behind integrating data and DevOps.



Expected Duration (hours)
0.8

Lesson Objectives

DevOps for Data Scientists: Data DevOps Concepts

  • define the use and application of DevOps for data science and machine learning
  • describe topological considerations for data science and DevOps
  • apply high-level organizational and cultural strategies for data science with DevOps
  • describe the specific day-to-day tasks of DevOps for data science
  • assess technological risks and uncertainties when implementing DevOps for data science
  • describe scaling approaches to data science using DevOps
  • identify how DevOps can improve communication for data science workflows
  • identify how DevOps can help overcome ad hoc approaches to data science
  • describe considerations for ETL pipeline workflow improvements through DevOps
  • describe the microservice approach to machine learning
  • create a diagram of your data science infrastructure
  • Course Number:
    it_dsdods_01_enus

    Expertise Level
    Intermediate