Continuous deployment

As explained in Machine Learning Platform Engineering

Continuous deployment is a software-delivery practice that automatically releases verified changes to a target execution environment.

Continuous deployment automates the release of tested application changes, replacing error-prone manual deployments with a repeatable path from code change to production service. section 3.4 section 3.6

A production-oriented deployment flow typically:

• Tests the application, builds a container image, and updates the Helm chart with the new image. section 3.4
• Deploys the updated chart to Kubernetes using the newly built image tag. section 3.4
• Monitors the deployed version and restores the latest intended image when an older deployment appears. section 3.4

GitLab CI and Argo CD

A GitLab CI job tests and builds an ML application, then updates its Helm chart. Argo CD deploys that chart to Kubernetes and monitors whether the running image matches the latest build. section 3.4

Figure 1.9 Model deployment consists of the container registry, CI/CD, and automation working in concert to deploy ML services.

Shows the container registry, CI/CD automation, and deployment workflow working together to release an ML service. section 1.3.3

How Machine Learning Platform Engineering covers it

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 it assumes

Builds from Docker and Kubernetes basics rather than assuming prior exposure to infrastructure tooling.

What you can do after

Operate/configure automated ML deployments that test, package, release, monitor, and correct application versions.

How it connects

Connects Docker, container registries, Kubernetes, Helm, GitLab CI, Argo CD, and application monitoring.

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Also explained in

• 

  liveBookmanning.com
  Effective Platform EngineeringContinuous deployment is a software delivery practice in which validated changes are automatically released to a target environment.
• 

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  Infrastructure as Code, Patterns and Practices: With examples in Python and TerraformContinuous deployment is a software delivery practice that automatically deploys changes to production after they pass the required validation steps.
• 

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  Learn Docker in a Month of Lunches, Second EditionContinuous deployment is the practice of automatically packaging and delivering validated software for deployment.
• 

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  Automating API DeliveryContinuous deployment is the practice of automatically deploying every change that passes all delivery-pipeline stages to production without human intervention.
• 

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  Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and TerraformContinuous deployment is a software delivery practice that automatically deploys changes to a target environment after they pass the required validation.

Compare how 6 books explain continuous deployment →

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.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Platform Engineering. The section links point to the parts of the book it draws on.