This course contains the use of artificial intelligence. You have been told to make the data AI-ready, and nobody has told you what that means. The warehouse already works. Every dashboard reconciles, every test passes, the numbers have been signed off for years. Then the first model goes in front of a real decision and it is useless, and nothing in your monitoring says why.
This course is the architecture underneath that problem, taught end to end on one commercial lender – Larkfield Commercial Finance, invented for this course, with synthetic data you download and run. Six layers: the replay floor, the conformed core, three gold stores rather than one, the semantic layer, the serving stores, and the governed gateway an agent has to call through. The argument the whole course turns on is that the unit of this architecture is not the layer, it is the boundary between layers. A layer is a promise about what is true, with an owner and a test. An unowned boundary is the thing that actually rots, and you can find them in any architecture diagram in about ten minutes once you know what to look for.
You will see why a warehouse that satisfies every BI stakeholder still fails a model: BI asks what is true when you query, and a model asks what was knowable at a past instant. Change one join from a plain equality to an as-of predicate and the measured AUC on this course’s own data falls from 0.9334 to 0.6997. The first number is what leaks, the second is what the model can actually do. Both come out of a script that ships with the course.
