Overton: A Data System for Monitoring and Improving Machine-Learned Products
authors Christopher Ré, Feng Niu, Pallavi Gudipati, Charles Srisuwananukorn
We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems.
Apple Scholars is a program created to recognize the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. As part of Apple Scholars, Apple is proud to announce the recipients of PhD fellowships in AI/ML. In recognition of these outstanding PhD students, each will receive support for their research and academic travel for two years, internship opportunities, and a two-year mentorship with an Apple researcher in their field. Nominated students were selected based on their innovative research, record as thought leaders and collaborators in their fields, and unique commitment to take risks and push the envelope in machine learning and AI.