  Machine learning (ML) has proven itself in high-value web applications such
as search ranking and is emerging as a powerful tool in a much broader range of
enterprise scenarios including voice recognition and conversational
understanding for customer support, autotuning for videoconferencing,
intelligent feedback loops in large-scale sysops, manufacturing and autonomous
vehicle management, complex financial predictions, just to name a few.
Meanwhile, as the value of data is increasingly recognized and monetized,
concerns about securing valuable data and risks to individual privacy have been
growing. Consequently, rigorous data management has emerged as a key
requirement in enterprise settings. How will these trends (ML growing
popularity, and stricter data governance) intersect? What are the unmet
requirements for applying ML in enterprise settings? What are the technical
challenges for the DB community to solve? In this paper, we present our vision
of how ML and database systems are likely to come together, and early steps we
take towards making this vision a reality.
