Senthil Bogana brings over two decades of expertise in advanced computing architectures, large-scale data warehousing, and analytics infrastructure to power strategic product innovation. He has successfully led transformative data initiatives at industry-leading organizations including Meta, T-Mobile, and Infosys. A Senior Member of IEEE and a Fellow of Sigma Xi, Senthil is also a respected mentor, speaker, and editorial board member of the International Journal of Computer Science and Information Technology Research. His thought leadership extends across global technology conferences and academic publications, where he actively contributes to shaping the future of data and computing
Data First: Crafting Datasets for Effective ML Models
Machine Learning (ML) models are rapidly transforming industries—from healthcare and finance to transportation and cybersecurity. As their adoption accelerates, ensuring these models are accurate, reliable, and capable of generalizing to real-world scenarios becomes paramount. One of the most critical determinants of model success lies in the training process—particularly the quality, relevance, and diversity of the underlying data. In this session, I will share insights on the pivotal role data plays in shaping ML performance, emphasizing the need for strategic dataset selection, meticulous preprocessing, and rigorous validation practices. These elements are essential for building scalable, high-impact ML systems that deliver consistent and trustworthy outcomes across dynamic environments.