Rapid and Scalable Bayesian AB Testing
AuthorsSrivas Chennu,* Andrew Maher,* Christian Pangerl, Subash Prabanantham, Jae Hyeon Bae, Jamie Martin, Bud Goswami
AB testing aids business operators with their decision making, and is considered the gold standard method for learning from data to improve digital user experiences. However, there is usually a gap between the requirements of practitioners, and the constraints imposed by the statistical hypothesis testing methodologies commonly used for analysis of AB tests. These include the lack of statistical power in multivariate designs with many factors, correlations between these factors, the need of sequential testing for early stopping, and the inability to pool knowledge from past tests. Here, we propose a solution that applies hierarchical Bayesian estimation to address the above limitations. In comparison to the current sequential AB testing methodology, we increase statistical power by exploiting correlations between factors, enabling sequential testing and progressive early stopping without incurring excessive false positive risk. We also demonstrate how this methodology can be extended to enable the extraction of composite global learnings from past AB tests, to accelerate future tests. We underpin our work with a solid theoretical framework that articulates the value of hierarchical estimation. We demonstrate its utility using both numerical simulations and a large set of real-world AB tests. Together, these results highlight the practical value of our approach for statistical inference in the technology industry.
Scene analysis is an integral core technology that powers many features and experiences in the Apple ecosystem. From visual content search to powerful memories marking special occasions in one’s life, outputs (or "signals") produced by scene analysis are critical to how users interface with the photos on their devices. Deploying dedicated models for each of these individual features is inefficient as many of these models can benefit from sharing resources. We present how we developed Apple Neural Scene Analyzer (ANSA), a unified backbone to build and maintain scene analysis workflows in production. This was an important step towards enabling Apple to be among the first in the industry to deploy fully client-side scene analysis in 2016.
Providing new features—while preserving user privacy—requires techniques for learning from private and anonymized user feedback. To learn quickly and accurately, we develop and employ statistical learning algorithms that help us overcome multiple challenges that arise from sampling noise, applications of differential privacy, and delays that may be present in the data. These algorithms enable teams at Apple to measure and understand which user experiences are the best. This understanding leads to continual improvements across Apple's products and services to drive better experiences. We provide aspects of this understanding to the Apple developer community through features such as product page optimization.