DevOps is a collective term for the culture and practices that integrate software development (Development) and operations (Operations), achieving both faster release cycles and improved quality through CI/CD pipelines and automation tools.
In traditional software development, a division of labor was common: the development team wrote the code, and the operations team deployed it to the production environment. The problem lies in the "wall" this division creates. The development side wants to ship new features quickly; the operations side wants to maintain stability. Their interests conflict, and friction arises with every release.
DevOps tears down this wall. Developers become mindful of operations, and operations staff get involved in the development process. The essence of DevOps is building a culture where both sides share the same goal——"deliver value to users quickly, and fix problems quickly when they arise."
CI/CD (Continuous Integration / Continuous Delivery): A pipeline that automatically builds, tests, and deploys code changes. It frees teams from the anxiety of manual deployments.
Infrastructure as Code (IaC): Managing server and network configurations as code using tools like Terraform or Pulumi. The question "Who changed this server's configuration, and when?" becomes a thing of the past.
Monitoring and Observability: Using tools like Datadog, Grafana, and OpenTelemetry to visualize system status in real time. The goal is to reduce the time from incident detection to recovery (MTTR).
As LLMs and AI agents are increasingly deployed in production, the scope of DevOps is expanding as well. Operational challenges that did not exist in traditional applications have emerged——such as model version control, autoscaling of inference servers, and A/B testing of prompts. MLOps was born out of this context, and DevSecOps integrates a security perspective into the mix.


DevSecOps is an approach that integrates security measures into the DevOps pipeline from the outset, unifying the three domains of development, security, and operations.

MLOps is a practice that automates and standardizes the entire lifecycle of machine learning model development, training, deployment, and monitoring, enabling the continuous operation of models in production environments.

ATDD (Acceptance Test-Driven Development) is a development methodology in which the entire team defines acceptance test criteria before development begins, automates those tests, and then proceeds with implementation.

What is PoC Development? From the Basics of Proof of Concept to Costs, Process, and How to Choose the Right Outsourcing Partner

Shift Left is a development approach that moves processes such as testing, security checks, and quality validation to earlier stages of the development lifecycle, thereby reducing the cost of detecting and fixing defects.