Indie Degree

AI Engineering

A self-directed programme. Progress is banked and only ever goes up — there is no streak to break.

You are reading someone else's transcript. It is public on purpose — a credential nobody can inspect is not a credential — and only its owner can tick anything off.

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Patterns for Building LLM-based Systems & Products

AIE-101 · reading · 60 min — one sitting

Demonstrated: 0 / 326
Capabilities: 0 / 12
Artifacts: 0
Defended: 0

Read and watched: 6 of 232 items. Tracked, and deliberately counted apart from the figures above — exposure is how you close a gap, not evidence that it closed.

4 of 55 credits · 8.4h logged · 942h of required work left. Planning figures, kept below the ones that mean something. Averaging 1h a week. At that rate the remaining required work lands in roughly 637–1592 weeks. A band around your own measured pace, not a statistical interval.

What this is for

0 of 12 capabilities earned

Each one is a line you could put in a CV — and the evidence it would take to defend it to someone who sets the questions.

The skill graph — 75 skills, 93 prerequisites, 8 levels deep. What has to come before what.Calibration — whether this learner's own scoring can be trusted, measured against the panel on every judged item.

Applied AI Engineering — in study order

The employable core. Closes the distance to applied and field AI roles, where the gap read already puts the learner at 94%.

Numbered by study order, which deliberately does not follow the course codes — the sequence is value-weighted within prerequisite constraints, so stopping at any point leaves the highest-value skills already banked.

Depth — in study order

What makes this a programme rather than a certificate. Builds the models rather than calling them.