You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

In modern MLOps/CPMAI pipelines, a dedicated retraining pipeline monitors data drift and model performance, then automatically re-ingests updated data, re-trains, and redeploys improved models. This pipeline enables controlled iteration while preserving reproducibility and governance, directly addressing the “model iteration” requirement stated.

A. Acquisition captures raw data but does not iterate models.
C. Feature engineering creates predictors once data are present; it is inside, not across, iterations.
D. ELT moves and transforms data for storage; it lacks model retraining logic.

1. Google Cloud AI Platform Pipelines, “Automated Retraining Pipelines”, Section 4.1 (2023).

2. Cognilytica CPMAI v7 Guide, Phase 5 “Model Development”, Activity 5.6 “Establish Automated Retraining Pipeline”, pp. 78-79.

3. Microsoft Learn “MLOps v2”, Module “Automated model retraining”, steps 1-3.