Blog Posts

A Short History of Prediction-Serving Systems

Daniel Crankshaw blog, Uncategorized 0 Comments

Machine learning is an enabling technology that transforms data into solutions by extracting patterns that generalize to new data. Much of machine learning can be reduced to learning a model — a function that maps an input (e.g. a photo) to a prediction (e.g. objects in the photo). Once trained, these models can be used to make predictions on new inputs (e.g., new photos) and as part of more complex decisions (e.g., whether to promote a photo). While there are thousands of papers published each year on how to design and train models, there is surprisingly less research on how to manage and deploy such models once they are trained. It is this later, often overlooked, topic that we discuss…

Low-Latency Model Serving with Clipper

Daniel Crankshaw blog

The mission of the RISELab is to develop technologies that enable applications to make low-latency decisions on live data with strong security. One of the first steps towards achieving this goal is to study techniques to evaluate machine learning models and quickly render predictions. This missing piece of machine learning infrastructure, the prediction serving system, is critical to delivering real-time and intelligent applications and services. As we studied the prediction-serving problem, two key challenges emerged. The first challenge is supporting the stringent performance demands of interactive serving workloads. As machine learning models improve they are increasingly being applied in business critical settings and user-facing interactive applications. This requires models to render predictions that can meet the strict latency requirements of…