Adding LLMOps to the LLM Twin

In the previous section, we saw how to set up the infrastructure for the LLM Twin project by manually building the Docker image and pushing it to ECR. We want to automate the entire process and implement a CI/CD pipeline using GitHub Actions and a CT pipeline using ZenML. As mentioned earlier, implementing a CI/CD/CT pipeline ensures that each feature pushed to main branches is consistent and tested. Also, by automating the deployment and training, you support collaboration, save time, and reduce human errors.

Finally, at the end of the section, we will show you how to implement a prompt monitoring pipeline using Opik from Comet ML and an alerting system using ZenML. This prompt monitoring pipeline will help us debug and analyze the RAG and LLM logic. As LLM systems are non-deterministic, capturing and storing the prompt traces is essential for monitoring your ML logic.

Before diving into the implementation, let’s start with a quick section...