Author: Aaron Cheng

The past decade has seen rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for many successful use cases. Beyond AI's “cute” factor that can automatically differentiate between a dog and a cat in photos, these new technologies are already deployed in real-world applications to generate impactful business outcomes. AI and Machine Learning predict lending risk, provide product recommendations, analyze customer churn behaviors, and manage inventories. While technology giants like Google and Amazon continue harvesting the great benefits of AI, many traditional businesses are still struggling to adopt AI. One key challenge businesses face is that data is often not ready for AI/ML, and preparing it would take too much time and effort, something they cannot afford.  ML input data must be a single flat table, but real-world data is often distributed across different tables and multiple databases. Combining data from disparate tables, generating new…