AI agents are emerging as an important trend since they enable levels of business adaptability, flexibility and agility that traditional AI systems can’t achieve. This flexibility is valuable in unpredictable environments where real-time monitoring and control aren’t practical. Autonomous behaviors, despite significant societal, legal and ethical implications, are the answer to the increasing complexity in our enterprise systems.

D&A leaders scaling generative AI training and inference must navigate new technologies including GPUs and AI processors, both on-premises and in the cloud. In this session, we provide a pragmatic framework to map GenAI model sizes to infrastructures across on-premises and cloud environments.

The enormous potential business value of AI is not going to materialize spontaneously. AI leaders should guide their organization toward an era in which AI is not only creating tangible business value but goes beyond to become a critical competitive differentiator and industry disruptor.

Moving AI projects from pilot to production requires substantial effort for most enterprises. AI Engineering provides the foundation for enterprise delivery of AI and generative AI solutions at scale unifying DataOps, MLOps and DevOps practices. This session will highlight AI engineering best practices across these dimensions covering people, processes and technology.