I'm a bit lost here, sorry.
What will your QA team audit for? Generally, these audits will consider compliance to a standard (e.g., IEC 62304) or basic software engineering practices (configuration status accounting). An audit would likely be done irrespective of what you do regarding deployment (or acceptance).
With machine learning, you're generally in a state of perpetually verifying that the model still works after new information is learned. This is often running known materials through and ensuring the results have not been negatively affected. This, though, isn't an audit (or necessarily done by SW QA - but they may audit to see if you're doing what you said you'd do in terms of re-verification).
The error prediction concept is interesting. There are a lot of static analysis tools already that look for typical coding errors. I'm curious how your tool will continually learn and predict errors.
