Automated Testing for LLMOps

Automated Testing for LLMOps is a free, roughly one-hour DeepLearning.AI short course built and taught by Rob Zuber, CTO of CircleCI, and it is best understood as a focused introduction to evaluating LLM applications inside a continuous integration pipeline rather than a broad MLOps course. It teaches you to write rules-based and model-graded (LLM-as-a-judge) evaluations and to wire them into a CircleCI workflow that runs automatically on every change, gating commits, pull requests, and deployments. The strongest part is the concrete, hands-on framing: you see real evaluation code and an actual CI config rather than abstract theory, and the curriculum maps cleanly onto how CircleCI documents LLM evaluation gating in production. The main caveats are its narrowness and vendor specificity (the CI examples are CircleCI-centric and assume an OpenAI-style app), the lack of a certificate, and the fact that the official pages publish no public star rating, so the catalog's 4.4 cannot be independently verified. It is a high-value free hour for engineers already building LLM apps, and largely skippable for total beginners who have never shipped one.

It is an excellent, free, tightly-scoped hour for developers who already build LLM applications and want a real pattern for automated evaluation in CI, but it is narrow and CircleCI/OpenAI-specific, assumes Python plus prior LLM-app experience, and offers no certificate, so it is conditional on you already having that background and wanting exactly the eval-in-CI topic.

Best for: Software and ML engineers who are already building LLM-powered applications (e.g., with OpenAI APIs and Python) and want a concrete, hands-on pattern for automating quality checks. It suits people setting up CI/CD for AI features, anyone wanting to catch hallucinations, data drift, or harmful output before deployment, and CircleCI users specifically, since the orchestration examples target CircleCI workflows.

Skip if: Complete beginners who have never built an LLM app or written Python, learners who need a certificate of completion, and teams locked into a different CI system (GitHub Actions, GitLab CI, Jenkins) who want vendor-neutral tooling. It is also not a fit for those seeking deep coverage of production monitoring, observability dashboards, or full MLOps lifecycle, since it concentrates on the evaluation-in-CI slice.