DevOps

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

DevOps is a software delivery practice that integrates development and operations work through collaboration, automation, and shared responsibility.

DevOps bridges software development and operations through collaboration and automation. It reduces the mismatch between development assumptions and production reality while supporting rapid, adaptive delivery. section 1.3

A DevOps delivery approach emphasizes:

• Automating software development and delivery processes so teams can focus more on development as the project matures. section 1.3
• Reducing the cost and risk of late changes by improving information flow into project activities. section 1.3
• Organizing work through sprints, backlogs, end-of-sprint checklists, and accumulating documentation that supports production maintenance and governance. section 1.4

Sprint-based machine-learning delivery

Structure an ML project as successive sprints, record tasks in a backlog, verify completion with a checklist, and retain documentation for team reuse, production maintenance, and governance. A running Bike Shop case study connects these artifacts to project decisions. section 1.4

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

Use the generic DevOps production and delivery process to trace how automation and information flow support iterative software delivery. section 1.3

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

How deep it goes

Explains the development–operations mismatch, automation rationale, and project artifacts, but does not specify implementation technologies.

Examples

Uses generic delivery processes, sprints, backlogs, checklists, documentation portfolios, and the Bike Shop case study.

What it assumes

Builds from agile development and extends its iterative delivery practices toward operations, production, and machine-learning work.

What you can do after

Apply it by automating delivery work, organizing ML tasks into sprints, and preserving documentation for production governance.

How it connects

Links agile development with automation, software operations, documentation, machine-learning projects, production systems, and governance.

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Related concepts

• deployment pipelineincludes · A sequence of automated or manual stages that builds, tests or validates, packages, and delivers software or models toward deployment.
• GenAIOpsused by · GenAIOps is the operational discipline of applying DevOps practices to develop, deploy, monitor, and maintain generative AI applications.
• APIOpsuses · APIOps is the practice of automating the governance, delivery, and operation of API products across their life cycle.

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.