  Google AlphaGos win has significantly motivated and sped up machine learning
(ML) research and development, which led to tremendous ML technical advances
and wider adoptions in various domains (e.g., Finance, Health, Defense, and
Education). These advances have resulted in numerous new concepts and
technologies, which are too many for people to catch up to and even make them
confused, especially for newcomers to the ML area. This paper is aimed to
present a clear picture of the state-of-the-art of the existing ML technologies
with a comprehensive survey. We lay out this survey by viewing ML as a MLOps
(ML Operations) process, where the key concepts and activities are collected
and elaborated with representative works and surveys. We hope that this paper
can serve as a quick reference manual (a survey of surveys) for newcomers
(e.g., researchers, practitioners) of ML to get an overview of the MLOps
process, as well as a good understanding of the key technologies used in each
step of the ML process, and know where to find more details.
