  With the increasing trend of Machine Learning (ML) enabled software
applications, the paradigm of ML Operations (MLOps) has gained tremendous
attention of researchers and practitioners. MLOps encompasses the practices and
technologies for streamlining the resources and monitoring needs of
operationalizing ML models. Software development practitioners need access to
the detailed and easily understandable knowledge of MLOps workflows, practices,
challenges and solutions to effectively and efficiently support the adoption of
MLOps. Whilst the academic and industry literature on the MLOps has been
growing rapidly, there have been relatively a few attempts at systematically
synthesizing and analyzing the vast amount of existing literature of MLOps for
improving ease of access and understanding. We conducted a Multivocal
Literature Review (MLR) of 150 relevant academic studies and 48 gray literature
to provide a comprehensive body of knowledge on MLOps. Through this MLR, we
identified the emerging MLOps practices, adoption challenges and solutions
related to various areas, including development and operation of complex
pipelines, managing production at scale, managing artifacts, and ensuring
quality, security, governance, and ethical aspects. We also report the
socio-technical aspect of MLOps relating to diverse roles involved and
collaboration practices across them through the MLOps lifecycle. We assert that
this MLR provides valuable insights to researchers and practitioners seeking to
navigate the rapidly evolving landscape of MLOps. We also identify the open
issues that need to be addressed in order to advance the current
state-of-the-art of MLOps.
