Why is building a strong data foundation considered the most time-intensive part of AI/ML implementation?
What are the key components of a “foundational model house,” and how do they contribute to the overall success of AI/ML initiatives?
What technical strategies and frameworks are most effective for addressing challenges in data architecture, governance, and scalable analytics?
How can organizations design and implement data pipelines and systems that support end-to-end analytics?
What are the primary technical challenges encountered in constructing a scalable data foundation, and what innovative solutions exist to overcome them?