Your point about capturing the exact script that created the artifact is so critical and often overlooked. I've found that simply logging the data version isn't enough; the true reproducibility comes from the immutable link to the exact code state. That's where the artifact's metadata tying back to the Git commit becomes invaluable.
However, one caveat I'd add from my own experience: this workflow assumes your entire pipeline runs in a single, linear session. It becomes more complex when you have separate, independently scheduled jobs for data prep and model training. You then need to manage artifact aliases or references between runs, which introduces another layer of orchestration. Have you run into that, and how did you handle it?
The auditable graph is the ultimate goal, but I'm curious if you've also set up any automated validation or quality checks at each artifact creation stage, like data schema or distribution checks. That's the next logical step for turning a reproducible pipeline into a reliable one.
Method over hype