Part 8/14:
He further advocates for capturing interaction history, like purchase logs or customer feedback, to deliver richer, more relevant insights. Embedding constraints from business workflows ensures that data queries reflect operational realities, thus making analytics more actionable.
Building Intelligent, Reusable Assets with AI and ML
Vun points out the importance of tagging ML models, data assets, and workflows systematically. Tools like MLflow facilitate this orchestration, but the overarching goal is to build knowledge assets—like feature stores—that are ML-ready and context-aware.