"Your models are only as good as the data they’re trained on. In AI/ML, we spend 80% of our time cleaning data, yet most quality issues stem from poor database design. Duplicate records, broken joins, and inconsistent schemas silently corrupt feature stores and ML pipelines long before you write a single line of code. This first part of my 3-part series explores why database design is the invisible architecture behind every successful AI/ML system. We’ll cover how poorly designed databases derail models, how relational databases (RDBMS) enforce data quality, and how concepts like primary keys, foreign keys, and constraints directly impact feature engineering, scalability, and compliance. Strong databases mean strong models. Weak databases mean wasted months. Read the full post to learn how to build AI/ML pipelines on solid database foundations."