Machine learning has been widely used in discovering patterns from complex and unstructured data. Successful machine learning needs vast amounts of data and labels for training and validation. Creating datasets and labels require significant efforts.  A team at Purdue University creates datasets using network cameras that can provide real-time visual data. These cameras can continuously stream live views of national parks, zoos, city halls, streets, university campuses, highways, shopping malls, and so on.  The stationary cameras (some of them have PTZ, pan-tilt-zoom) have contextual information (such as time and location) about the visual data. By cross-referencing with other sources of data (such as weather and event calendar), it is possible to label the data automatically.  The run-time system allocates and adjusts computing resources as needed. This system is a foundation for many research topics related to analyzing visual data, such as (1) whether today’s technologies are ready analyzing the versatile data, (2) what computing infrastructure is needed to handle the vast amount of real-time data, (3) where are the performance bottlenecks and how hardware accelerators (such as GPU) can improve performance, (4) how can this system automatically produce labels for machine learning.