Bridging the performance gap in data infrastructure for AI [Video]

A significant chasm exists between most organizations’ current data infrastructure capabilities and those necessary to effectively support AI workloads.

Credit: nuchao

In the current technology landscape, organizations are looking to AI to provide transformative product differentiation and groundbreaking new revenue streams. In 2023, large language models (LLMs) dazzled folks with the possibility of new capabilities, features, and products. In 2024 and beyond, we’re now focused on the reality of bringing those ideas to fruition and the challenges of what that means for data infrastructure. For most, the road to AI success is not smooth, as organizations find their legacy data ecosystem will not suffice for data processing today, let alone tomorrow.

As the need for data as a differentiator builds, organizations are grappling with the daunting task of modernizing their infrastructure and phasing out legacy systems, while concurrently delivering traditional analytics without interruption. Yet delivering new value through data is pivotal for augmenting AI capabilities …