How a Stronger Data Infrastructure Results in Better AI Outcomes

When developers move a pilot AI project into production, they often quickly discover weaknesses in their data infrastructure. They might have high volumes of pertinent data, but is that data readily accessible to the AI model? Do they know its provenance and whether it has been validated? And when they run a model, can they reproduce the results — and roll back an operation if it goes awry? When those capabilities are lacking, an AI initiative can slow to a crawl, leaving data, systems and people sitting idle, resulting in a poor return on an agency’s AI investments.

A data control plane provides a robust foundation for the data infrastructure. The control plane sits on top of an agency’s existing storage and manages the data lifecycle, governance and unified access for all AI-ready data. With these controls in place, an agency can shift to an AI factory model, creating a reliable, repeatable process for taking AI initiatives from concept to deployment.

“It’s not just about being able to store it and retrieve it,” said Oz Katz, Co-Founder and CTO at lakeFS. “It’s also about all the controls that go around it.”

In this video interview, Katz discusses how a stronger data infrastructure can set up agencies for AI success. Topics include:

• The hidden costs of not having AI-ready data
• The building blocks of a sustainable AI factory
• The key capabilities of a data control plane