Small Language Models (SLMs) provide a viable alternative to Large Language Models (LLMs) for developing high-performing, cost-effective, and secure generative AI solutions. CDAOs, AI architects, and data architects should attend this presentation to gain insights into the strengths and weaknesses of SLMs and discover five specific use cases where SLMs outperform LLMs.

This session will look at the quickly emerging domain of agentic AI. What are AI agents? What are the solutions and applications that will most benefit from an agent-based approach? What are the pitfalls to watch for when considering this fast-growing software engineering discipline? Join this session to know the answers to such questions and more.

This session will look at the quickly emerging domain of agentic AI. What are AI agents? What are the solutions and applications that will most benefit from an agent-based approach? What are the pitfalls to watch for when considering this fast-growing software engineering discipline? Join this session to know the answers to such questions and more.

Creating a business case for GenAI is hard. GenAI initiatives produce outcomes that range from everyday to game-changing and cannot be described by a single currency of value. Gartner has identified three business case types and recommends CIOs create a value portfolio that encompasses the variety of GenAI investments enterprises make. Come to this session to learn how to frame your GenAI business case and align executive expectations on value creation from GenAI.

AI explainability is crucial for fostering trust and creating responsible AI by providing transparency into decision-making processes, enabling accountability and mitigating biases. Join this session to know about some practices that D&A technical professionals must adopt to enhance AI explainability and trust, and ensure the responsible and accountable use of AI.

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.