DevOps for Data Science

This course explains how DevOps practices can be applied to data science to support documentation, data and model refresh, testing, performance monitoring, security, and compliance. It covers operationalization, source control, infrastructure as code, automation, continuous integration, continuous testing, continuous delivery, and pipelines.

Syllabus

Introduction
Who is Damian Brady
Data Science and DevOps
What is DevOps
DevOps Metrics
DevOps Process
Data Science Definition
Data Science Cycle
Data Science Processes
Data Science Life Cycles
Languages and Tools
Operationalization
Source Control
Refactor
Infrastructure as Code
Automation
Continuous Integration
Continuous Testing
Continuous Delivery
Summary
Pipelines
Shift Left
Ignite
Jupiter Notebooks
Data Breach Notebooks
Data Breach Starter Mix
Data Types