Summary
In this chapter, we introduced MLOps, a DevOps-like workflow for developing, deploying, and operating ML services. DevOps stands for a quick and high-quality way of making changes to code and deploying these changes to production.
We first learned that Azure DevOps gives us all the features to run powerful CI/CD pipelines. We can run either build pipelines, where steps are coded in YAML, or release pipelines, which are configured in the UI. Release pipelines can have manual or multiple automatic triggers (for example, a commit in the version control repository or if the artifact of a model registry was updated) and create an output artifact for release or deployment.
Version-controlling your code is necessary, but it's not enough to run proper CI/CD pipelines. In order to create reproducible builds, we need to make sure that the dataset is also versioned and pseudo-random generators are seeded with a specified parameter. Environments and infrastructure should also...