Data Quality’s Impact on Large Language Models
Speakers

Mona Rakibe, CEO and Co-Founder of Telmai, an AI-based data observability platform built for Open Architecture. A veteran in the Data Infrastructure landscape, previously Mona Rakibe worked in engineering and product leadership positions that fueled product innovation and growth strategies in the dynamic landscape of startups and enterprises like Reltio, EMC, Oracle, and BEA, where AI-driven solutions have played a pivotal role.

Max is a Co-founder and CTO of Telmai (telm.ai). He holds a Ph.D. in Computer Science (Database theory) and spent over a decade building SaaS products focusing on big data analytics and ML in various startups at different stages, from inception to acquisition and IPO.

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.
SUMMARY
Data quality is the foundation of successful Generative A, traditional ML, and data-driven initiatives. In this talk, I will share our research results on this as there is a tangible impact of poor data quality on model performance and training cost.

