AI

From Data Lakes to AI Hubs: Architecting the Intelligence Layer of the Enterprise

Data lakes were a breakthrough in storing massive amounts of information. They made reporting and business intelligence easier and centralized enterprise data in one place. But when it comes to machine learning, they fall short. Teams struggle to access consistent, timely features, creating friction between data engineers and ML engineers. Training and production often operate on different data, slowing deployment and reducing impact. The solution is a shift from passive storage to active intelligence platforms. AI hubs do more than hold data. They make sure everyone follows the same standards, keep the pipelines running without hiccups, and let teams grab the features they need without waiting. Instead of just storing data, AI hubs focus on how work actually gets done, so insights reach the people who need them quickly. For…