Standard AI models rely on the assumption that real-world data matches training patterns (IID). However, in the complex landscape of biology, this assumption frequently breaks. Valuable signals such as unexpected drug responses or rare patient profiles, often appear as "Out-of-Distribution" (OOD) data, which traditional models struggle to interpret. Join Elucidata’s team as we present a data-centric AI framework designed to navigate these challenges. We will explore why "data is the hero" and how prioritizing data quality over model size is essential for reliable performance in the "long tail" of scientific discovery. The session will focus on three key pillars for solving OOD problems:

• High-quality Data Infrastructure: Fueling models with clean, linked, and context-rich data to improve performance on rare, small-sample problems.
• Federated Learning: Leveraging decentralized training to learn from diverse, private datasets and handle distributional shifts.
• Physics-based Rules: Integrating first principles to help models reason beyond their training corpus.

The webinar will also explore AI tools like Ultra Deep Research, highlight how we build and deploy various AI agents, share real-world lessons from the field, and feature insights on dealing with OOD outliers into the next generation of innovation.