  Recently software development companies started to embrace Machine Learning
(ML) techniques for introducing a series of advanced functionality in their
products such as personalisation of the user experience, improved search,
content recommendation and automation. The technical challenges for tackling
these problems are heavily researched in literature. A less studied area is a
pragmatic approach to the role of humans in a complex modern industrial
environment where ML based systems are developed. Key stakeholders affect the
system from inception and up to operation and maintenance. Product managers
want to embed "smart" experiences for their users and drive the decisions on
what should be built next; software engineers are challenged to build or
utilise ML software tools that require skills that are well outside of their
comfort zone; legal and risk departments may influence design choices and data
access; operations teams are requested to maintain ML systems which are
non-stationary in their nature and change behaviour over time; and finally ML
practitioners should communicate with all these stakeholders to successfully
build a reliable system. This paper discusses some of the challenges we faced
in Atlassian as we started investing more in the ML space.
