MLOps for Ads Ranking at Pinteres
This talk will discuss scaling ads ranking at Pinterest through machine learning algorithms and ML ecosystem innovations.
Join us for a talk on the journey of scaling ads ranking at Pinterest by innovations in machine learning algorithms and the ML ecosystem beginning from traditional logistic regressions to more complex transformers models, and advancements in ML platform offerings through unified feature representation, shared feature store, and standardized inference services. Throughout the process, we encountered various challenges and gained valuable lessons. Discover the hurdles we overcame and the insights we gained in this talk, as we share the transformation of ads ranking at Pinterest and the lessons learned along the way.
Speaker:
Aayush Mudgal is a Senior Machine Learning Engineer at Pinterest, currently leading the efforts around Privacy Aware Conversion Modeling. He has a successful track record of starting and executing 0 to 1 projects, including conversion optimization, video ads ranking, landing page optimization, and evolving the ads ranking from GBDT to DNN stack. His expertise is in large-scale recommendation systems, personalization, and ads marketplaces. Before entering the industry, Aayush conducted research on intelligent tutoring systems, developing data-driven feedback to aid students in learning computer programming. He holds a Master's in Computer Science from Columbia University and a Bachelor of Technology in Computer Science from Indian Institute of Technology Kanpur.
