Deployment Glossary

Browse 96 deployment terms defined in plain English, from the cultural dictionary of computing.

96 Deployment Terms

AI Deploy
To release or put an AI model or AI-powered feature into an environment where it can be used for testing or production workloads. AI deploy decisions usually...
AI Deployment
The operational process and resulting setup for serving an AI model or AI feature in staging or production. AI deployment includes packaging, routing, scaling,...
AI Edge
AI inference or processing performed close to where data is generated or consumed, such as on devices, local gateways, or edge servers rather than only in...
AOT Compilation
Ahead-of-Time compilation — translating high-level code to native machine code before deployment, producing a standalone binary. Eliminates cold-start latency,...
Application Container
A container image or runtime unit that packages an application with its dependencies and executes it in an isolated environment. Application containers...
blue-green deployment
A deployment strategy that maintains two identical production environments: 'blue' (current live) and 'green' (new version). Traffic is switched from blue to...
Blue-Green Deployment
Blue-green deployment is a release strategy that maintains two identical production environments, labeled blue and green. At any time, one environment serves...
Blue/Green Deployment
A deployment strategy using two identical production environments (blue and green). The current version runs on blue; the new version is deployed to green and...
Canary Analysis
Automated statistical comparison between a canary deployment (new version, small traffic percentage) and the baseline (current version) to detect regressions...
Canary Deployment
A deployment strategy that gradually rolls out changes to a small percentage of users before deploying to the entire fleet. Named after canaries in coal mines...
canary release
A deployment strategy where a new version is rolled out to a small subset of users or servers first, then gradually expanded if metrics look healthy. Named...
Cloud Container
A containerized workload running in a cloud-managed environment, often on orchestration platforms or serverless container services. Cloud containers package...
Cloud Deployment
The act of releasing an application, service, or infrastructure change into a cloud environment. Cloud deployments may involve containers, serverless...
Config Map
A named collection of configuration key-value pairs, especially in Kubernetes, used to inject non-secret settings into applications and containers at runtime....
Container Config
The runtime settings, environment variables, mounts, ports, resource limits, and entrypoint details that define how a container runs. Container config is...
Coolify
An open-source, self-hosted alternative to platforms like Vercel, Netlify, and Heroku. Coolify lets developers deploy applications, databases, and services on...
dark launch
A deployment technique where new code is released to production and executed in the background without exposing results to users. Dark launches allow teams to...
Edge AI
Running AI models directly on local devices (phones, cameras, IoT) rather than in the cloud, reducing latency and keeping data private. The reason your phone...
Feature Flag
A feature flag (also called a feature toggle) is a configuration mechanism that enables or disables application functionality at runtime without deploying new...
Feature Toggle
A runtime configuration switch that enables or disables a feature without deploying new code. Categories: release toggles (gradual rollout), experiment toggles...
Fly.io
A platform for deploying full-stack applications and databases close to users worldwide using lightweight virtual machines (Firecracker microVMs). Unlike...
GitOps
An operational framework where the desired state of infrastructure is declared in a Git repository, and automated processes continuously reconcile the actual...
GitOps Pull Model
A GitOps implementation where an agent running inside the cluster (ArgoCD, Flux) continuously polls a Git repository and reconciles the cluster state to match...
guardrails
Safety mechanisms and filters applied to AI systems to prevent harmful, off-topic, or policy-violating outputs. Guardrails can be implemented at the prompt...
Helm Chart
A package format for Kubernetes applications containing templated YAML manifests, default configuration values, and metadata. Helm charts enable versioned,...
Hotfix
Hotfix is an urgent software patch deployed directly to production to address a critical bug, security vulnerability, or outage without going through the full...
Immutable Deploy
A deployment model where servers are never updated in place — instead, a new machine image (AMI, Docker image) is built with the new code and replaces the old...
immutable infrastructure
An infrastructure management approach where servers are never modified after deployment — any change requires building a new server image and replacing the old...
Kustomize
A Kubernetes configuration customization tool that layers patches and overlays onto base manifests.
LLMOps
The operational discipline around deploying, monitoring, evaluating, and maintaining large-language-model systems in production.
Mixed Precision Training
Mixed Precision Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for...
MLflow
MLflow is an MLOps platform for experiment tracking, model packaging, and lifecycle management. It is commonly used for recording runs, registering models, and...
Model Architecture
Model Architecture is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Card Detail
Model Card Detail is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Compression
Model Compression is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Deployment
The process and resulting setup for releasing a model into an environment where it can serve real workloads. Model deployment includes packaging, routing,...
Model Distillation
Model Distillation is a training approach where a smaller student model learns from a larger teacher model. It is commonly used for compressing models while...
Model Ensemble
Model Ensemble is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Evaluation
Model Evaluation is an evaluation concept used to measure model quality, robustness, or efficiency. It is commonly used for comparing systems before release or...
Model Explainability
Model Explainability is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Governance
Model Governance is a safety or governance control for limiting harmful, noncompliant, or insecure model behavior. It is commonly used for production AI...
Model Hub
Model Hub is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Inference
Model Inference is the phase where a trained model processes new inputs to produce predictions or generations. It is commonly used for production APIs, batch...
Model Merging
Model Merging is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Parallelism
Model Parallelism is a systems strategy for splitting model computation or parameters across devices. It is commonly used for training and serving workloads...
Model Poisoning
Model Poisoning is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Pruning
Model Pruning is a compression technique that removes parameters, channels, or structures judged less important. It is commonly used for shrinking models for...
Model Quantization
Model Quantization is a technique that reduces numerical precision so models use less memory and compute. It is commonly used for deploying large models on...
Model Registry
Model Registry is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
model serving
The infrastructure and process of deploying trained machine learning models to production so they can receive input data and return predictions in real time or...
Model Serving Detail
Model Serving Detail is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Sharding
Model Sharding is a systems strategy for splitting model computation or parameters across devices. It is commonly used for training and serving workloads that...
Model Stacking
Model Stacking is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Training
Model Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Model Versioning
Model Versioning is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Zoo
Model Zoo is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Multi-GPU Training
Multi-GPU Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Non-Autoregressive Model
Non-Autoregressive Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building...
ONNX Runtime
A runtime for executing models in the ONNX format across different hardware backends with an emphasis on portability and inference performance.
Open Source Model
Open Source Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Open Weight Model
Open Weight Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Port Binding
The practice of a service binding to a network port to listen for incoming requests, making itself available as a self-contained, externally accessible...
Post-Deploy Hook
A script or automation step that executes automatically after a deployment completes, typically used for tasks like cache invalidation, database migrations,...
Post-Training
Post-Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Pre-Deploy Hook
A script or automation step executed before a deployment begins, commonly used to run final test suites, create database backups, enable maintenance pages, or...
Pre-Training
Pre-Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Pretraining Data
Pretraining Data is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Probabilistic Model
Probabilistic Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Process Manager
A daemon or tool that starts, monitors, and restarts application processes, ensuring they stay running and are properly managed. Examples include systemd, PM2...
Production Build
A compiled and optimized version of an application intended for end users, with minification, tree-shaking, dead code elimination, source map separation, and...
Production Environment
The live infrastructure — servers, databases, CDNs, and services — where the application serves real users and processes real data, as distinguished from...
Quantization
Reducing the precision of a model's numerical weights (e.g., from 32-bit to 4-bit) to decrease memory usage and increase inference speed, with minimal quality...
Quantization Aware Training
Quantization Aware Training is a technique that reduces numerical precision so models use less memory and compute. It is commonly used for deploying large...
Railway
A modern application hosting platform that simplifies deploying backend services, databases, and full-stack applications. Railway provides a visual dashboard...
Release Build
A compiled version of software with optimizations enabled, debug symbols stripped, assertions removed, and minification or tree-shaking applied — producing...
Release Channel
A named distribution track (such as stable, beta, canary, or nightly) that determines which version of software a user receives, allowing teams to ship...
Release Security
The protections applied to the software release process, including build integrity, artifact signing, approval controls, provenance, and safe deployment...
Rollback
Rollback is the process of reverting a system, database, or deployment to a previous known-good state after a failed update, bug, or incident. In database...
Rolling Update
A deployment strategy that gradually replaces old instances with new ones, maintaining availability throughout. Typically configured with maxSurge (extra...
Runtime Environment
The complete set of software, libraries, configuration, and system resources available to an application during execution — including the runtime itself,...
Scalar Quantization
Scalar Quantization is a technique that reduces numerical precision so models use less memory and compute. It is commonly used for deploying large models on...
Ship It Squirrel
A meme originating from GitHub's Campfire chat featuring a squirrel wearing a pilot's helmet, posted in code reviews to signal 'looks good, let's deploy.' The...
Smoke Test
Smoke Test is a preliminary, broad-but-shallow test run performed to verify that the most critical functions of a software system work before committing to...
Staged Rollout
A deployment strategy that gradually increases the percentage of users or servers receiving a new version — typically progressing through stages like 1%, 5%,...
Staging Environment
A pre-production environment that mirrors the production infrastructure as closely as possible — same services, configuration, and (ideally) anonymized...
State Space Model
State Space Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Static Binary
An executable that has all of its library dependencies compiled and linked directly into the binary at build time, requiring no shared libraries on the target...
Teacher Model
Teacher Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Training Compute
Training Compute is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Training Data Detail
Training Data Detail is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Training Loop
Training Loop is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Training Run
Training Run is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Upgrade
The process of replacing a software component — such as a library, framework, runtime, or operating system — with a newer version, often involving dependency...
Vector Quantization
Vector Quantization is a technique that reduces numerical precision so models use less memory and compute. It is commonly used for deploying large models on...
Vercel
A cloud platform for frontend developers that specializes in deploying web applications with zero configuration. Founded by Guillermo Rauch and originally...
World Model
World Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...

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