GenAI is as promising as it is challenging. To stay on top, scientists will have to combine data, infrastructure, models, and subject-matter expertise into a formidable base. While Gen AI is incredibly exciting, what does it really take to get these models into production? How can we keep trusting the results? How does one select the right problem? In this session, Swetabh gives us a breakdown of the tools needed to set your GenAI initiatives up for success.