머신 러닝 엔지니어
Current# Kakao Corp. AI Model Development Team1. Large Language Model End2End Training- Include Pretraining language model (PLM), Supervised fine tuning(SFT), RLHF (Reinforcement Learning From Human Feedback)- Preprocessing dataset for PLM.- PLM multi-node training and performance evaluation- Managing human labeler to create datasets for SFT and RLHF- Research on datasets that work well for SFT and Training# Kakao Corp. Recommendation Team1. Sequential RecommendationAchievements- Build transformer based sequential recommendation models from scratch.- Build c++ based dataset builder for model to execlerate preprocessing time on large dataset- Find domains where the sequential recommendation system works well through experiments.2. Recsys Challenge 2022Achievements- I conducted an experiment to see if the sequential model works well on the session based dataset, and it works well.- In the form of [view]*N + [Purchase], data was augmented by data augmentation in the form of last view prediction in addition to purchase prediction, and significant results occurred in performance. (public leaderboard ranked 15th -> 13th.)