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.
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.
What is a CI/CD pipeline? CI/CD for machine learning DevOps. Learn about CI/CD pipelines, how they improve the software development lifecycle, and Algorithmia’s CI/CD solution for machine learning.
Getting Started with scikit-learn Pipelines for Machine Learning: Building a pipeline from the ground up. (All code in this post is also included in this GitHub repository.)
Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. The Pipelines can also
In the course of the last years the interest in Data Science and Machine Learning has continuously increased. Thanks to libraries like Scikit-Learn.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant