  We demonstrate that, with the availability of distributed computation
platforms such as Amazon Web Services and open-source tools, it is possible for
a small engineering team to build, launch and maintain a cost-effective,
large-scale visual search system with widely available tools. We also
demonstrate, through a comprehensive set of live experiments at Pinterest, that
content recommendation powered by visual search improve user engagement. By
sharing our implementation details and the experiences learned from launching a
commercial visual search engines from scratch, we hope visual search are more
widely incorporated into today's commercial applications.
