Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

Video streaming is crucial for AI applications that gather videos from sources to servers for
inference by deep neural nets (DNNs). Unlike traditional video streaming that optimizes …

We present gg, a framework and a set of command-line tools that helps people execute
everyday applications—eg, software compilation, unit tests, video encoding, or object …

The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …

As video volumes grow, analysts are increasingly able to query the real world. Since
manually watching these growing volumes of video is infeasible, analysts have increasingly …

The availability of vast video collections and the accuracy of ML models has generated
significant interest in video analytics systems. Since naively processing all frames using …

Video-analytics-as-a-service is becoming an important offering for cloud providers. A key
concern in such services is privacy of the videos being analyzed. While trusted execution …

Video analytics systems perform automatic events, movements, and actions recognition in a
video and make it possible to execute queries on the video. As a result of a large number of …

Machine learning models are widely deployed in production cloud to provide online
inference services. Efficiently deploying inference services requires careful tuning of …

We present VStore, a data store for supporting fast, resource-efficient analytics over large
archival videos. VStore manages video ingestion, storage, retrieval, and consumption. It …