Introduction to Artificial Intelligence

Agentic AI is software that wraps a language model in a loop where it can request actions from real tools, read what each action returns, and choose the next move until the job is finished or a rule stops it.

MLOps is the engineering practice of keeping a trained model useful in production: recording what produced it, serving it, watching for the day its answers stop matching the world, and retraining or reverting before anyone downstream is harmed by the gap.

Responsible AI is the practice of building and running systems whose decisions you could defend to the person on the receiving end of them, covering fairness, transparency, privacy and accountability.

Data quality is the degree to which a dataset is fit for the purpose it is used for, assessed along named dimensions, whether the records are complete, unique, consistent, timely, valid and accurate, and measured against the requirements of the decision the data supports rather than against an absolute standard.

Data modelling is the design of how data is structured for a given use: which entities are recorded, which attributes describe them, how the tables relate through keys, and, for analytical use, which table holds the measurements and which hold the context, decided before the data is loaded so that the questions the model must answer run correctly and fast.

Hypothesis testing is the statistical procedure for deciding whether an observed difference, between two groups, two periods or a sample and a claimed value, is larger than chance variation would produce if there were no real difference, by computing how probable a result at least that extreme would be under that assumption.