Course details

DevOps for Data Scientists: Deploying Data DevOps

DevOps for Data Scientists: Deploying Data DevOps


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

In this course, learners will explore deploying data models into production through serialization, packaging, deployment, and rollback. You will begin by watching how to serialize models using Python and Pandas. Then the 8-video course takes a look at the tools and approaches to model packaging and deployment. Next, you will explore the concept of the blue-green deployment strategy for data DevOps, which is the strategy for upgrading running software. This leads into examining the concepts behind the Canary deployment strategy in terms of data DevOps. Canary deployments can be regarded as a phase or test rollout on updates and new features. Then take a look at versioning and approaches to rolling back models for machine learning with DevOps. Finally, you will learn about some of the considerations for deploying models to web APIs (application programming interfaces). The concluding exercise involves creating a model by using Python and Pandas, then serializing the results of the model to a file.



Expected Duration (hours)
0.6

Lesson Objectives

DevOps for Data Scientists: Deploying Data DevOps

  • Course Overview
  • serialize models using Python and pickle
  • describe tools and approaches to model packaging and deployment
  • describe the blue-green deployment strategy for Data DevOps
  • describe the canary deployment strategy for Data DevOps
  • describe approaches to rolling back model versions
  • explore approaches to deploying models to web APIs
  • use python and pandas to serialize a model
  • Course Number:
    it_dsdods_03_enus

    Expertise Level
    Intermediate