Summary
In this chapter, we learned how to integrate AI and ML into our DevOps pipelines. We discussed the basic requirements and steps for implementing AI-enabled DevOps, starting with access to source repositories, creating data lakes, initiating and training data models, and follow-up recommendations and actions. We also learned that AI-enabled DevOps is a stage in digital transformation, but that enterprises need to set out a roadmap that eventually allows them to integrate AI and ML into their development and deployment processes. AI-driven development and operations are at the peak of innovation in digital transformation.
Next, we introduced some tools that will help us in implementing AI-enabled DevOps. We learned that it's a fast-growing market where major cloud providers try to integrate their native DevOps tools with AI and ML. Examples include Kubeflow by Google, CodeGuru by AWS, and MLOps by Microsoft Azure.
Finally, we discussed the readiness assessment for...