Continuous integration, delivery, and deployment in MLOps
Automation is the primary reason for CI/CD in the MLOps workflow. The goal of enabling continuous delivery to the ML service is to maintain data and source code versions of the models, enable triggers to perform necessary jobs in parallel, build artifacts, and release deployments for production. Several cloud vendors are promoting DevOps services to monitor ML services and models in production, as well as orchestrate with other services on the cloud. Using CI and CD, we can enable continual learning, which is critical for a ML system's success. Without continual learning, a ML system is deemed to end up as a failed Proof of Concept (PoC).
In order to learn to deploy a model in production with continual learning capabilities, we will explore CI, CD, and continuous delivery methods.
As you can see in Figure 7.1, CI is key to CD...
