Enterprises are shifting focus from pure model accuracy to robust infrastructure for deploying AI in production, according to Nishanth Prakash of Oracle Cloud Infrastructure. In a recent talk, Prakash emphasized treating AI platforms as products, requiring strong security, low-latency streaming, and comprehensive governance to move beyond experimental prototypes.
The discussion highlights the practical limitations of current AI systems and the need for scalable, reliable deployment pipelines. Key considerations include designing for security from the ground up, ensuring low-latency responses for real-time applications, and implementing governance frameworks to maintain trust and compliance.
"The real challenge isn't building a model that works in a notebook—it's deploying it securely and scaling it reliably," Prakash said. The talk advocates for an operationalized approach where AI platforms are engineered with the same rigor as any critical enterprise system.