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5JNLGZVU
Authors: Christopher Ré, Feng Niu, Pallavi Gudipati, Charles Srisuwananukorn
Published: 2019-09-07
Type: preprint
Collections: Papers
Links: View on Zotero ↗ · DOI ↗ · Source PDF ↗
Abstract
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× versus production systems.