Building an NLP system is only half the job; you also need to know whether it is good, whether it is fair, and whether it can run reliably for real users. This final module covers all three. It starts with how language models are evaluated, from automatic metrics and LLM-as-a-judge checks to standard benchmarks, and what a benchmark score does and does not tell you.
It then turns to bias: a Stanford lecture on how human bias enters the data and models behind language technology. The last lesson looks at deployment, the practices used to move a model from a notebook into production and keep it working there.
Watch the lessons in order, then put everything together in the course Projects.
An overview of the main ways to evaluate model outputs automatically, including ground-truth metrics, semantic similarity, LLM-as-a-judge, rule-based checks and functional tests.
Video 5minExplains what standard benchmarks such as MMLU measure, how to interpret the scores, and how to run an evaluation yourself with the lm-evaluation-harness.
Video 31minA beginner-friendly overview of MLOps: how models move from development to production, with CI/CD and tools such as Docker, Kubernetes and MLflow.
Video 12min