AI governance is no longer optional. This session will explore practical strategies for embedding AI governance directly into the design and development of AI architectures, ensuring that compliance, transparency and ethical standards are upheld from the ground up.

RAG has emerged as a powerful approach for building advanced AI systems that combine the strengths of large language models with external knowledge sources. However, RAG solutions struggle with reliability and require a lot of experimentation. This session will address key questions to help determine the best design pattern and optimization for RAG implementations.

GenAI solutions include several choices and trade-offs. A critical decision is: should you build custom AI solutions in-house or buy off-the-shelf products? This session brings together a debate on the trade-offs, risk and rewards of each approach. The session will be based on scenarios and use-cases to highlight key considerations such as cost, reliability , flexibility and speed for different decisions such as LLMs vs. SLMs, RAG vs. AI agents, packaged platform capability vs. bespoke custom solution, packaged vs. open-source.

RAG has emerged as a powerful approach for building advanced AI systems that combine the strengths of large language models with external knowledge sources. However, RAG solutions struggle with reliability and require a lot of experimentation. This session will address key questions to help determine the best design pattern and optimization for RAG implementations.

Securing and derisking AI requires new processes, tools and mindsets. Risks abound internally and externally while regulations take hold. This session outlines frameworks, tools and processes for managing AI trust, risk and security.

AI systems can have significant impact in organizations decisions and product offerings. This has triggered a complex regulatory landscape and related organizational guidance to ensure responsible and ethical implementation of AI systems. This session provides the technology framework supporting the processes and policies for a reliable AI system that is fair, safe and secure.

Despite the popularity of retrieval-augmented generation, organizations are struggling to optimize large language model applications based on RAG. Attend this session to get suggestions and recommendations on improving your RAG solution, LLMOps for RAG systems, and scaling considerations.

AI architects are challenged to define a technology roadmap in an environment that is currently defined by a rapid pace of AI technology, specifically with generative AI initiatives, evolving product capabilities, needs for up-skilling, all of this with the goal of improving business outcomes. This session will provide the framework, architectures and tools to define such a roadmap.

Despite the popularity of retrieval-augmented generation, organizations are struggling to optimize large language model applications based on RAG. Attend this session to get suggestions and recommendations on improving your RAG solution, LLMOps for RAG systems and scaling considerations.