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Deployment pipeline

As explained in Delivering Machine Learning Projects: From design to deployment

CI/CD is the practice of automating continuous integration, testing, and delivery or deployment of software changes.

A deployment pipeline automates the movement of system artifacts into production and verifies the transfer with post-release tests. Avoid relying on informal instructions for a stressful release. section 9.8

Make the release an explicit, repeatable operation:

  • Automate promotion of all required files to production. section 9.8
  • Plan and automate post-release tests, then run them and verify the results. section 9.8
  • Prepare authorized key people to intervene if deployment goes wrong. section 9.8

Release tooling

A release may be started by clicking a button or issuing a command-line instruction; Jenkins is one example of a standard pipeline tool. section 9.8

Figure 1.3 A generic DevOps production and delivery process (modeled after Ebert, 2016; redrawn and amended by the author)

View the generic DevOps production and delivery process as context for deployment automation. section 1.3

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Details artifact transfer, test planning and execution, and contingency readiness, but not pipeline configuration.
Examples
Uses Jenkins, button- or command-triggered releases, and automated post-release tests as examples.
What you can do after
Operate/configure it by automating artifact promotion, planning release tests, and preparing responders for deployment failures.
How it connects
Links pipelines with MLOps, version control, data transformation, testing, and model deployment.

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This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.