LLM Evaluation & MLOps
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About this course
LLM Evaluation & MLOps teaches you to measure whether an LLM feature works and to ship changes to it safely. The first lesson scores a small support-reply generator on a ten-example test set with an exact-match check and an LLM-as-judge rubric, and shows why the two disagree. From there the course covers building eval datasets, reference-based and semantic metrics, judge rubrics and their biases, RAG and agent evaluation, safety and robustness tests, the statistics that tell a real regression from noise, online evaluation and A/B tests, MLflow experiment tracking and prompt versioning, tracing and cost monitoring, and the CI gates, canary releases and rollbacks that keep a prompt change from breaking production. The course has 167 lessons and about 1113 minutes of content. Each lesson is a short narrated reading card followed by MCQ practice with an explanation for every answer.
How the lessons work
Each lesson starts from something concrete: a handful of test cases and their scores, a judge prompt and the scores it returns, a trace of one slow request, or two prompt versions with overlapping confidence intervals. The idea is explained against that example, then MCQ practice asks which metric fits, why a judge favoured the longer answer, or whether a two-point drop is a regression, with an explanation for every answer. Code is Python and kept small, so the focus stays on the decision rather than on a framework.
Who this course is for
Python developers, ML engineers, students and freshers who already call an LLM API and now have to answer the question every team asks next: is the new prompt or model actually better? It suits backend engineers who own an AI feature, data scientists moving LLM prototypes to production, and QA engineers asked to test non-deterministic systems. If you are new to how LLMs work, start with GenAI Engineering: LLM Foundations; if you build retrieval systems, GenAI Engineering: RAG Systems pairs well with the RAG evaluation topic here.
MCQ practice vs video courses
Video courses on LLMOps tend to demo one evaluation tool on one dataset. The hard part in real work is judgement: whether ten examples are enough, whether a judge is biased toward its own model, whether a metric rewards the wrong thing, or whether a canary is safe to widen. This course puts you in those situations with a small example and asks what you would conclude, then explains why. That builds the habits that stop a team shipping a regression it could have measured.
Best way to learn LLM evaluation
Pick one LLM feature you have built or used, even a toy one, and write ten golden test cases for it before you start. As you work through the course, add an exact-match check, a judge rubric, a confidence interval and a CI gate to that same feature. Answer each MCQ before reading its explanation. By the deployment topic you will have a small but complete evaluation loop that you can reuse at work.