The exploding field of artificial intelligence (AI) and machine learning (ML) promises transformative potential for industry, heralding a potential new era of efficiency and innovation. However, the journey from theoretical models to practical applications is fraught with challenges that often temper the initial excitement. Even with successful Proofs-of-Concept in practical, real-world opportunities, operationalization and long-term maintenance can make emotions from âtemperedâ to âfrustrated.â This talk will discuss the realities concerning the challenges of bringing AI research and experimentation to operational applications across various types of AI: inferential, optimization, and Generative AI.
