Kajanan Sangaralingam
Head of Data Science, Mobilewalla
Kajanan Sangaralingam manages the Data Science and AI function at Mobilewalla. He is passionate about solving real business problems using innovative AI/machine learning approaches. Prior to Mobilewalla, Kajanan worked as a Senior Data Scientist at Singapore Telecommunications where he honed his skills processing and analyzing large volumes of structured and unstructured data. He earned his Ph.D. at the National University of Singapore and his Bachelor of Science in Information Technology degree at the University of Moratuwa, Sri Lanka. His early work experience included many roles as a Senior Software Engineer and Software Engineer at companies in various industries.
Sessions
Of all the choices made by data scientists in the course of building and operating models, feature engineering & selection is one of the most critical. Features have a substantive impact on a model’s quality, including its predictive accuracy and resilience. Unfortunately, as most ML scientists and practitioners are aware, feature engineering is more art than science. It is ad-hoc, messy, error-prone and ends up consuming 70-80% of the time and effort when building models, often resulting in sub-optimal feature selection leading to low-quality models. In this tutorial, we will introduce new ways of performing feature engineering, turning it into a systematic, procedural and scalable process, which is substantively more efficient than how it occurs currently. Participants will perform a hands-on, end-to-end, feature building exercise, with particular emphasis on feature engineering using Anovos (https://anovos.ai/ or https://github.com/anovos/anovos)