  In e-commerce, content quality of the product catalog plays a key role in
delivering a satisfactory experience to the customers. In particular, visual
content such as product images influences customers' engagement and purchase
decisions. With the rapid growth of e-commerce and the advent of artificial
intelligence, traditional content management systems are giving way to
automated scalable systems. In this paper, we present a machine learning driven
visual content management system for extremely large e-commerce catalogs. For a
given product, the system aggregates images from various suppliers, understands
and analyzes them to produce a superior image set with optimal image count and
quality, and arranges them in an order tailored to the demands of the
customers. The system makes use of an array of technologies, ranging from deep
learning to traditional computer vision, at different stages of analysis. In
this paper, we outline how the system works and discuss the unique challenges
related to applying machine learning techniques to real-world data from
e-commerce domain. We emphasize how we tune state-of-the-art image
classification techniques to develop solutions custom made for a massive,
diverse, and constantly evolving product catalog. We also provide the details
of how we measure the system's impact on various customer engagement metrics.
