CI/CD refers to the application of Continuous Integration and Continuous Deployment techniques in the realm of Machine Learning. These practices play a critical role in increasing productivity and efficiency of teams and organizations in managing ML projects.
Continuous Integration (CI) involves automating the integration of code changes from multiple contributors into a single software project. Continuous Deployment (CD) is the automatic deployment of integrated code to a production environment.
How CI/CD works in practice
CI/CD pipeline for Machine Learning (ML) enables automation and monitoring of all steps in the software delivery process. The process begins with the version control system that tracks all changes. When a new code is committed to this system, the CI server automatically triggers a build and testing phase for the current ML model to ensure its integrity.
After successful build and test, the Continuous Deployment begins. The CD pipeline ensures that automated processes for deploying and provisioning environments run smoothly. It can also provide automated data verification, model training, evaluation, validation and deployment.
In essence, the CI/CD pipeline works by continuously integrating ML code, data, and environment configuration changes, running automated testing to ensure the system is working properly, and deploying the ML model to a production environment. The goal is to catch and fix errors more quickly and efficiently, ensure that the software is always in a state that can be reliably released, and automate the process of software release.
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