Clipper is a low-latency online prediction serving system designed to handle large-scale machine learning models and real-time inference requirements. Developed as part of the AMPLab project, Clipper is optimized for serving predictions with response times as low as 20 milliseconds, making it ideal for applications that demand fast decision-making capabilities.
Unlike traditional systems that focus on offline and batch processing, Clipper is designed for online and latency-optimized scenarios where quick inference is crucial. By separating the model training and inference phases, Clipper ensures that real-time prediction requests are handled efficiently and with minimal delay. This enables users to make decisions and run queries on their data in near real-time, significantly improving the speed and performance of their predictive models.
Clipper is heavily studied in the Big Data and machine learning communities, with a strong focus on optimizing the serving process for large-scale models. By leveraging technologies like Spark, Clipper is able to scale to handle massive amounts of data and complex machine learning algorithms, making it a valuable tool for organizations looking to deploy predictive models in production environments.
Overall, Clipper represents a significant advancement in the field of online prediction serving systems, offering a solution for handling real-time inference with low latency and high performance. Its ability to support a wide range of machine learning applications and decision-making processes makes it a valuable tool for organizations seeking to harness the power of their data for faster and more accurate decision-making.
