Clipper: A Low-Latency Online Prediction Serving System

TL;DR

Clipper is a versatile, low-latency prediction serving system that enhances real-time machine learning deployment by integrating caching, batching, and adaptive model selection, without modifying underlying frameworks.

Contribution

Introduces Clipper, a modular prediction serving system that improves latency, throughput, and robustness across various frameworks without requiring changes to models.

Findings

Achieves low-latency predictions on benchmark datasets.

Demonstrates comparable performance to TensorFlow Serving.

Enables model composition and online learning for improved accuracy.

Abstract

Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment. In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system. Interposing between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks and applications. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. We evaluate Clipper on four common machine learning benchmark datasets and demonstrate its ability to meet the latency, accuracy, and throughput demands of online serving applications. Finally, we compare Clipper to the TensorFlow Serving system and demonstrate that we are able to achieve comparable throughput and latency while enabling model composition and online learning to improve accuracy and render more robust predictions.