DevOps on English AI Terms Dictionary

Serverless

Sat, 18 Jul 2026 10:15:20 +0000

Definition

Serverless architecture allows developers to build and run applications without managing server infrastructure. The cloud provider automatically scales resources up or down based on demand, charging users only for the compute time they consume. While servers still exist, their management is abstracted away. This model supports event-driven computing, enabling functions to trigger automatically in response to specific events, such as database changes or HTTP requests, reducing operational overhead significantly.

Rate Limiting

Sat, 18 Jul 2026 10:13:36 +0000

Definition

Rate limiting protects AI services and APIs from abuse, overload, and excessive resource consumption. It ensures fair usage among users and maintains system stability by capping throughput. Common strategies include token bucket, leaky bucket, and fixed window counters. In AI deployments, it is critical for managing inference costs and preventing Denial of Service (DoS) attacks on sensitive models.

Summary

An engineering control mechanism that restricts the number of requests a client can make to a service within a specific time window.

Linter

Sat, 18 Jul 2026 10:05:14 +0000

Definition

A linter is a utility that performs static analysis on source code without executing it. It checks for syntax errors, potential bugs, code smells, and deviations from style guides or best practices. By integrating linters into development workflows, teams ensure code consistency, improve readability, and catch issues early in the software development lifecycle. Popular examples include ESLint for JavaScript, Pylint for Python, and RuboCop for Ruby, which help maintain high-quality, maintainable codebases across large projects.

Kubernetes

Sat, 18 Jul 2026 10:04:10 +0000

Definition

Kubernetes (often abbreviated as K8s) is a container orchestration system originally developed by Google. It automates the deployment, scaling, and operation of application containers across clusters of hosts. By managing resources efficiently, ensuring high availability, and handling rolling updates and rollbacks, Kubernetes allows developers to focus on building software rather than managing infrastructure. It is a cornerstone of modern cloud-native development, supporting microservices architectures and enabling seamless integration with various cloud providers.

Continuous Deployment

Sat, 18 Jul 2026 09:51:47 +0000

Definition

Continuous Deployment is an extension of continuous delivery that automates the entire release process. Once code changes pass all quality gates, including unit tests, integration tests, and security scans, they are immediately deployed to the live production environment without manual intervention. This practice accelerates feedback loops, reduces time-to-market, and ensures that software updates are delivered frequently and reliably to end-users.

Summary

A software engineering practice where every code change that passes automated testing is automatically released to production.

Docker

Sat, 18 Jul 2026 09:40:59 +0000

Definition

Docker enables developers to package an application with all its dependencies into a standardized unit for software development. These containers isolate software from its environment, ensuring consistent performance across different computing environments. By abstracting away the underlying infrastructure, Docker simplifies deployment, scaling, and management of AI models and services, reducing the ‘it works on my machine’ problem common in complex machine learning pipelines.

Summary

Docker is a platform for developing, shipping, and running applications in lightweight, portable containers.

Continuous Integration

Sat, 18 Jul 2026 09:40:26 +0000

Definition

Continuous Integration (CI) is a critical DevOps practice that automates the integration of code changes from multiple contributors into a single software project. By running automated builds and tests immediately after each commit, CI helps detect integration errors early, improves software quality, and reduces the time required to validate new releases. It forms the foundation for Continuous Delivery and Deployment pipelines in modern AI engineering workflows.

Summary

A software development practice where developers frequently merge code changes into a central repository, triggering automated builds and tests.