Building a Scalable Data Lake for AI Projects

AI projects rarely fail because models are weak. More often, they fail because the data foundation cannot support growth. Teams may succeed with early experiments, only to hit limits when data volume increases, use cases multiply, or real-time access becomes essential. At that point, infrastructure decisions made early begin to show cracks. This is why […]