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Recently software development companies started to embrace Machine Learning(ML) techniques for introducing a series of advanced functionality in theirproducts such as personalisation of the user experience, improved search,content recommendation and automation. The technical challenges for tacklingthese problems are heavily researched in literature. A less studied area is apragmatic approach to the role of humans in a complex modern industrialenvironment where ML based systems are developed. Key stakeholders affect thesystem from inception and up to operation and maintenance. Product managerswant to embed "smart" experiences for their users and drive the decisions onwhat should be built next; software engineers are challenged to build orutilise ML software tools that require skills that are well outside of theircomfort zone; legal and risk departments may influence design choices and dataaccess; operations teams are requested to maintain ML systems which arenon-stationary in their nature and change behaviour over time; and finally MLpractitioners should communicate with all these stakeholders to successfullybuild a reliable system. This paper discusses some of the challenges we facedin Atlassian as we started investing more in the ML space.

Beyond the technical challenges for deploying Machine Learning solutions in a software company