Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model. What critical error did your team make?

CPMAI Phase VI stresses that every model promoted to production must be stored under a unique, immutable version tag so that any consuming application can roll back or compare results. Over-writing the first model erased that historical version, making re-deployment impossible and directly causing the downstream performance drop.

A. Data-governance oversees data lineage and policy, not the retention of code/model artefacts.
C. A retraining pipeline governs future training, but versioning is required even without retraining.
D. “Iteration” refers to improving model quality; the error was losing the previous artefact, not lack of iterative work.

1. Cognilytica, “Cognitive Project Management for AI (CPMAI) v7 Methodology Guide,” §6.3 Model Versioning, pp. 83-84, 2023.

2. Stanford University, CS329S Lecture 4, “ML Production & Deployment,” slide 27, 2021.

3. Sculley et al., “Hidden Technical Debt in Machine Learning Systems,” NIPS 2015, §3.2.