Modern AI systems fail not only because of flawed models but because evaluation is often treated as a one-time task rather than an ongoing discipline. This session addresses the challenge of scaling evaluation across teams and pipelines to ensure model reliability, fairness, and performance. Drawing from real-world experience in large-scale financial analytics, Anusha Dwivedula will examine how product and data teams can collaborate to design a continuous evaluation framework that integrates precision, recall, and drift metrics with observability, lineage, and quality controls. She will...
Anusha Dwivedula
Director of Product, Analytics
Morningstar
Bridging vision and execution in the age of AI, Anusha Dwivedula is a technologist, product leader, and data strategist known for turning complexity into clarity. As Director of Product, Analytics at Morningstar, she leads managed investment, portfolio, and risk analytics initiatives that power investment intelligence. Anusha played a pivotal role in Morningstar’s migration to AWS, architecting enterprise-wide platforms that modernized legacy systems, scaled data observability, and accelerated insight generation. Her career is defined by building high-impact data products in regulated industries and aligning business strategy with technical execution. Anusha has published multiple IEEE papers, including in IEEE CAI and IEEE CISOSE, and has been recognized as INFORMS Chicago “Professional of the Year 2024” and Women Impact Tech “Leader of Impact 2025.” She is also an AI 2030 Global Fellow advancing responsible AI governance.