Clipper: A Low-Latency Online Prediction Serving System (1612.03079v2)

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.

• The paper introduces Clipper, a system that delivers low-latency, high-throughput predictions through a layered, modular architecture for real-time ML deployment.
• The paper employs caching, adaptive batching, and bandit-based ensemble methods to optimize prediction efficiency and accuracy.
• The paper demonstrates robust performance with sub-20 ms latencies on benchmark datasets, offering advantages over traditional serving systems like TensorFlow Serving.

Clipper: A Low-Latency Online Prediction Serving System

The paper presents Clipper, a system designed to address the challenges of deploying machine learning models for real-time prediction services. While many machine learning frameworks focus primarily on model training, Clipper focuses on the deployment phase, providing low-latency, high-throughput, and accurate predictions across diverse machine learning frameworks.

Architecture and Design

Clipper introduces a layered architecture, comprising a model abstraction layer and a model selection layer. The abstraction layer provides a common prediction interface that abstracts the heterogeneity of machine learning frameworks. This is achieved through the use of a modular design that enhances ease of integration and flexibility, allowing for the transparent modification or replacement of models without impacting the application.

• Caching and adaptive batching techniques are employed to reduce latency and improve throughput. Caching serves frequent queries by avoiding re-evaluation, while adaptive batching improves throughput by optimizing batch sizes based on latency objectives.
• The model selection layer enhances accuracy and robustness. It employs techniques such as bandit algorithms and ensemble methods to dynamically select and combine predictions across models.

Evaluation and Results

The evaluation of Clipper was conducted on four benchmark datasets, demonstrating its capacity to maintain low and bounded prediction latencies (<20 ms), high throughput, and improved prediction accuracy through adaptive model selection. When compared to TensorFlow Serving, Clipper shows comparable performance in terms of throughput and latency, while offering additional features such as model composition and online learning.

Implications and Future Prospects

The introduction of Clipper addresses a critical challenge in machine learning: bridging the gap between model training and deployment. By providing a general-purpose, low-latency serving system, Clipper enables more widespread and efficient deployment of machine learning models across diverse application domains.

Clipper's layered architecture presents a flexible and robust approach to real-time prediction serving, effectively balancing the trade-offs between accuracy, latency, and throughput. The use of adaptive model selection and online learning techniques suggests potential for Clipper to further evolve, particularly in applications requiring personalization and quick adaptation to user feedback.

Future developments could explore scaling Clipper's architecture further, integrating more sophisticated model selection algorithms for enhanced performance and robustness. Additionally, expanding support to a broader range of machine learning frameworks would reinforce Clipper's versatility and utility in an ever-expanding landscape of AI applications.