PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
This addresses the performance inefficiencies in prediction serving systems for ML practitioners, offering significant improvements over existing black box methods.
The paper tackles the problem of optimizing machine learning prediction serving systems for low latency, high throughput, and graceful degradation by introducing PRETZEL, a white box architecture that enables end-to-end and multi-model optimizations. The result shows that PRETZEL reduces 99th percentile latency by 5.5x, memory footprint by 25x, and increases throughput by 4.7x compared to state-of-the-art approaches.
Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current prediction serving systems consider models as black boxes, whereby prediction-time-specific optimizations are ignored in favor of ease of deployment. In this paper, we present PRETZEL, a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations. Using production-like model pipelines, our experiments show that PRETZEL is able to introduce performance improvements over different dimensions; compared to state-of-the-art approaches PRETZEL is on average able to reduce 99th percentile latency by 5.5x while reducing memory footprint by 25x, and increasing throughput by 4.7x.