DevOps

Technical terms
About 1 min read

A culture and methodology that integrates software development (Dev) and IT operations (Ops) to automate software deployment and operations.

Also known as
DevOps

Detailed explanation

DevOps is a set of practices, tools, and a cultural philosophy that combines software development (Development) and IT operations (Operations) to shorten the development lifecycle and continuously deliver high-quality software. Its core elements include automated testing, continuous integration and deployment (CI/CD), infrastructure as code (IaC), and monitoring/logging. The goal is to bridge the gap between development and operations teams, increasing deployment frequency and reducing time to recovery. In the AI field, it extends to MLOps, which automates model training, deployment, and monitoring, and has recently evolved into DevSecOps, which integrates security.

Why It Matters in Tool Selection

To operate AI models in actual production, deployment, monitoring, and reproducibility must be established. Choosing tools that support DevOps practices (pipeline automation, version control, and monitoring integration) allows you to stably deploy models and quickly roll back when issues occur. Whether the AI tool integrates with existing CI/CD pipelines and infrastructure determines the operational burden.

What to Check When Choosing Tools

  • Can it integrate with and automate existing CI/CD pipelines?
  • Does it support deployment version control and rollbacks?
  • Does it connect with monitoring, logging, and alerting systems?
  • Can infrastructure be managed reproducibly as code (IaC)?

Real-World Examples

When deploying a new recommendation model, the DevOps pipeline automatically runs tests and applies it to a fraction of the traffic first to monitor metrics. If an anomaly is detected, it automatically rolls back to the previous version, updating safely with minimal impact on users.

Related terms

CI/CDMLOpsCloudDevSecOps