AutoML for Large Capacity Modeling of Meta's Ranking Systems

Hang Yin, Kuang-Hung Liu, Mengying Sun, Yuxin Chen, Buyun Zhang, Jiang Liu, Vivek Sehgal, Rudresh Rajnikant Panchal, Eugen Hotaj, Xi Liu, Daifeng Guo, Jamey Zhang, Zhou Wang, Shali Jiang, Huayu Li, Zhengxing Chen, Wen-Yen Chen, Jiyan Yang, Wei Wen

Abstract: Web-scale ranking systems at Meta serving billions of users is complex. Improving ranking models is essential but engineering heavy. Automated Machine Learning (AutoML) can potentially release engineers from labor intensive work of tuning ranking models; however, it is unknown if AutoML is efficient enough to meet tight production timeline in real-world applications and, at the same time, bring additional improvements to the already strong baselines. Moreover, to achieve higher ranking performance, there is an ever-increasing demand to scale up ranking models to even larger capacity, which imposes more challenges on the AutoML efficiency. The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (~100). This paper presents a sampling-based AutoML search method, focusing on neural architecture search and hyperparameter optimization, with a particular emphasis on addressing aforementioned challenges in Meta-scale production when building large capacity models. Our approach efficiently handles large-scale data demands. It leverages a lightweight predictor-based searcher and reinforcement learning to explore vast search spaces, significantly reducing the number of model evaluations. Through experiments in large capacity modeling for CTR and CVR applications, we have demonstrated that our method achieves outstanding Return-on-Investment (ROI) versus human tuned baselines, with up to 0.09% Normalized Entropy (NE) loss reduction or 25% Query-per-Second (QPS) increase by only sampling one hundred models on average from a curated search space. The proposed AutoML method has already made real-world impact where a discovered Instagram CTR model with up to -0.36% NE gain (over existing production baseline) was selected for large-scale online A/B test and show statistically significant gain. These production results proved AutoML efficacy and accelerated its adoption in ranking systems at Meta.