Training Data & the AI-Era Stack — Week 4 CheckWeek 4 of Data Engineering for ML & AI: training-data pipelines, vector infrastructure, and the data engineer's seat in the LLM stack.

Garbage-in-garbage-out is quantitatively worse for ML than for dashboards because:

Training-data pipelines at petabyte scale (Datology-style image/text corpora) are dominated by which classic DE concerns?

For RAG systems, the retrieval corpus is a data product with which uncomfortable property?

A feature's training set was built with today's feature values joined onto last year's events. The model validates brilliantly and fails in production. This is:

Labeling with LLMs (Uber's requirement-adherence work being one production case) changes the training-data pipeline how?

The cohort's closing thesis — where does the data engineer sit in the LLM stack?