HAPI Explorer: Comprehension, Discovery, and Explanation on History of ML APIs

Authors

  • Lingjiao Chen Stanford University
  • Zhihua Jin Hong Kong University of Science and Technology
  • Sabri Eyuboglu Stanford University
  • Huamin Qu Hong Kong University of Science and Technology
  • Christopher Ré Stanford University
  • Matei Zaharia Stanford University
  • James Zou Stanford University

DOI:

https://doi.org/10.1609/aaai.v37i13.27064

Keywords:

MLaaS, Model Evaluation, Explainable ML, ML Visualization, ML Dataset

Abstract

Machine learning prediction APIs offered by Google, Microsoft, Amazon, and many other providers have been continuously adopted in a plethora of applications, such as visual object detection, natural language comprehension, and speech recognition. Despite the importance of a systematic study and comparison of different APIs over time, this topic is currently under-explored because of the lack of data and user-friendly exploration tools. To address this issue, we present HAPI Explorer (History of API Explorer), an interactive system that offers easy access to millions of instances of commercial API applications collected in three years, prioritize attention on user-defined instance regimes, and explain interesting patterns across different APIs, subpopulations, and time periods via visual and natural languages. HAPI Explorer can facilitate further comprehension and exploitation of ML prediction APIs.

Downloads

Published

2023-09-06

How to Cite

Chen, L., Jin, Z., Eyuboglu, S., Qu, H., Ré, C., Zaharia, M., & Zou, J. (2023). HAPI Explorer: Comprehension, Discovery, and Explanation on History of ML APIs. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16416-16418. https://doi.org/10.1609/aaai.v37i13.27064