CI/CD Pipeline with Azure DevOps for Data Science project.

CI/CD Pipeline with Azure DevOps for Data Science project.

CI/CD Pipeline with Azure DevOps for Data Science project.: A CI/CD Pipeline implementation, or Continuous Integration/Continuous Deployment for Data science.

In this article, I would like to show how to build Continuous Integration and Continuous Delivery pipelines for a Machine Learning project with Azure DevOps.

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CI/CD pipeline

First of all, let’s define the CI/CD. As Wiki said “CI/CD bridges the gaps between development and operation activities and teams by enforcing automation in building, testing, and deployment of applications. Modern-day DevOps practices involve continuous development, continuous testing, continuous integration, continuous deployment, and continuous monitoring of software applications throughout its development life cycle. The CI/CD practice or CI/CD pipeline forms the backbone of modern-day DevOps operations.”

Ok, and let’s find out CI and CD separately.

Continuous integration is a coding philosophy and set of practices that drive development teams to implement small changes and check-in code to version control repositories frequently. Because most modern applications require developing code in different platforms and tools, the team needs a mechanism to integrate and validate its changes.

Continuous deliverypicks up where continuous integration ends. CD automates the delivery of applications to selected infrastructure environments. Most teams work with multiple environments other than the production, such as development and testing environments, and CD ensures there is an automated way to push code changes to them.

So, why it is important? Machine Learning applications are becoming popular in our industry, however, the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application.

machine-learning data-science azure ci-cd-pipeline devops

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