Summary
In this chapter, we have learned the key principles of continuous operations in MLOps, primarily, continuous integration, delivery, and deployment. We have learned this by performing a hands-on implementation of setting up a CI/CD pipeline and test environment using Azure DevOps. We have tested the pipeline for execution robustness and finally looked into some triggers to enhance the functionality of the pipeline and also set up a Git trigger for the test environment. This chapter serves as the foundation for continual operations in MLOps and equips you with the skills to automate the deployment pipelines of ML models for any given scenario on the cloud, with continual learning abilities in tune with your business.
In the next chapter, we will look into APIs, microservices, and what they have to offer for MLOps-based solutions.
