12 AI in DevOps engineering

 

This chapter covers

  • Integrating AI into existing DevOps workflows
  • Converting 7,000 PowerShell tests using AI tools
  • Selecting and using AI models for bulk code changes
  • Managing costs in mass code updates
  • Understanding GenAIOps fundamentals
  • Setting up AI-powered testing solutions

AI has reshaped DevOps and Platform Engineering over the past two years. It now helps with everything from deploying infrastructure to reviewing code changes.

We originally wanted to focus on GenAIOps—the practice of managing AI applications using DevOps principles. But we found it more useful to start with practical ways to add AI to your current DevOps work.

This chapter shows you real examples of AI in DevOps, like automating infrastructure and updating test suites. We’ll share what has worked for us, what problems we’ve run into, and what we’ve learned along the way.

12.1 Practical AI use cases in DevOps

As infrastructure engineers who build automation tools, we’re always looking for ways to simplify our work. We found three main ways to add AI to our workflow.

12.2 Upgrading nearly 7,000 tests: A real-world AI project

12.2.1 Choosing our tools

12.2.2 Developing an effective process

12.2.3 Implementation strategy

12.2.4 Managing large files

12.2.5 Breaking down instructions into focused passes

12.2.6 The results

12.2.7 Lessons learned

12.2.8 Beyond test updates: Other uses for AI-assisted development

12.3 The GenAIOps lifecycle

12.3.1 Applying GenAIOps: A practical workshop example

12.4 Prompts used to write this chapter

Summary