Cuneyd Kaya

The data scientist's role is undergoing a massive paradigm shift from traditional modeling to full-scale application operationalization and model deployment. With GenAI technologies, data scientists must upskill themselves to be able to build composite AI systems that include traditional AI techniques like machine learning and deep learning, as well as GenAI models. Join this session to know more.

While AI is gaining the attention of business leaders, data science remains an important business capability for driving decision making that should not be neglected. The choice is not between AI and data science but on how to combine predictive and prescriptive techniques so organizations can leverage the best of both worlds to create better solutions.

Organizations now have more options to build effective RAG systems, and those options come with confusion. Many organizations are looking to capitalize on new innovations such as long context windows, knowledge graphs, reasoning models, multi agent systems, and beyond. Attend this session to learn about seven challenges with RAG systems, their associated architectural choices, and best practices to improve their performance.