Continuous deployment
Continuous deployment is a software delivery practice in which validated changes are automatically released to a target environment, typically production, without manual approval.
Effective Platform Engineering
Continuous deployment is a software delivery practice in which validated changes are automatically released to a target environment.
- How it is used
- Automates the path from successful build through staging checks to production, reducing the delay between code completion and release.
- How deep it goes
- Describes pipeline stages and operational requirements, but does not specify pipeline configuration or a complete set of production checks.
- Examples
- Follows a service change through unit tests, integration tests, security scans, staging, and production checks.
- What you can do after
- Operate/configure it by establishing a repeatable, tested release path and maintaining consistent environments.
Read how Effective Platform Engineering explains continuous deployment →
Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform
Continuous deployment is a software delivery practice that automatically deploys changes to production after they pass the required validation steps.
- How it is used
- Automates infrastructure changes from testing to production when validation and recovery practices provide sufficient confidence.
- How deep it goes
- Explains testing, approval removal, change classification, and rollback expectations, but stops short of prescribing a specific implementation tool.
- Examples
- Uses server-tag updates, container-image upgrades, queues, monitoring alerts, scaling groups, and operating-system image rollbacks.
- What you can do after
- Operate automated infrastructure promotion and select approval requirements by change type.
Learn Docker in a Month of Lunches, Second Edition
Continuous deployment is the practice of automatically packaging and delivering validated software for deployment.
- How it is used
- Automates repository-triggered builds and validation in a cloud CI/CD workflow.
- How deep it goes
- Explains runners, YAML workflows, portable scripts, Docker builds, and test failures, but stops short of deployment-target configuration.
- Examples
- Uses a PowerShell build script, a GitHub workflow, multi-stage Docker tests, and a deliberately failing test.
- What you can do after
- Build it from scratch: configure a GitHub Actions workflow that runs portable build and test scripts after pushes.
Read how Learn Docker in a Month of Lunches, Second Edition explains continuous deployment →
Machine Learning Platform Engineering
Continuous deployment is a software-delivery practice that automatically releases verified changes to a target execution environment.
- How it is used
- Automates reliable ML application releases and replaces manual production deployments.
- How deep it goes
- Explains testing, image building, Helm updates, Kubernetes deployment, and version correction, without detailing pipeline implementation.
- Examples
- Uses ML services, container images, Helm charts, Kubernetes deployments, GitLab CI jobs, and Argo CD monitoring.
- What you can do after
- Operate/configure automated ML deployments that test, package, release, monitor, and correct application versions.
Read how Machine Learning Platform Engineering explains continuous deployment →
Automating API Delivery
Continuous deployment is the practice of automatically deploying every change that passes all delivery-pipeline stages to production without human intervention.
- How it is used
- Automates production delivery for every commit that passes the complete deployment pipeline.
- How deep it goes
- Explains pipeline stages, release candidates, higher-environment testing, and feature hiding, but does not provide tool-specific configuration.
- Examples
- Uses an Acme deployment-pipeline diagram and feature flags that conceal partially built features.
- What you can do after
- Operate/configure an automated pipeline that promotes passing commits through testing into production.
Read how Automating API Delivery explains continuous deployment →
Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and Terraform
Continuous deployment is a software delivery practice that automatically deploys changes to a target environment after they pass the required validation.
- How it is used
- Automates deployments to production or testing environments after code changes pass pipeline checks and tests.
- How deep it goes
- Explains pipeline validation, deployment-code reliability, and local testing, then applies them to a Kubernetes microservice workflow.
- Examples
- Uses a video-streaming microservice deployed to Kubernetes and separately applies MongoDB and RabbitMQ infrastructure configurations.
- What you can do after
- Build a GitHub Actions workflow that validates code changes and deploys a microservice to Kubernetes.
Related concepts
- deployment pipelinecontrasts with · A sequence of automated or manual stages that builds, tests or validates, packages, and delivers software or models toward deployment.
- productionuses · A production environment is the live operational setting in which software or other systems serve real users, workloads, or business processes.
- GitOpsused by · GitOps is an operational approach that uses version-controlled declarative configuration as the source of truth and automation to apply or reconcile system state.
- ArgoCDuses · Argo CD is a declarative GitOps continuous-delivery tool that deploys and synchronizes applications on Kubernetes from version-controlled configuration.
- software as a serviceused by · Software as a service is a software delivery model in which a provider hosts and operates applications that users access over a network.
- GitLab Pipelinesuses · GitLab Pipelines is GitLab’s continuous integration and delivery system for automating repository-based build, test, and deployment workflows.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.