AI Practitioner

Relational Deep Learning (RDL)  proposes a unified graph-based way to model multi-table databases for end-to-end learning using GNNs. This retains relational semantics, avoids joins, and supports temporal reasoning. It’s a paradigm shift that bridges the gap between ML and databases. 1. Motivation: From Tables to Graphs Traditional Setup Relational databases store structured data across multiple normalized tables , each capturing different types of entities (e.g., users, orders, products). These tables are linked by foreign-key (FK) and primary-key (PK) constraints. To train machine learning models, these databases are typically flattened into a single table using joins , and domain experts manually select and engineer features. Problems: Joins are expensive and brittle (schema changes break pipelines). Manual feature engineering is time-consuming and lacks relational awareness. Loss of information about cross-entity relationships . 2. Core Idea: Learn Direc...