  Following the development of digitization, a growing number of large Original
Equipment Manufacturers (OEMs) are adapting computer vision or natural language
processing in a wide range of applications such as anomaly detection and
quality inspection in plants. Deployment of such a system is becoming an
extremely important topic. Our work starts with the least-automated deployment
technologies of machine learning systems includes several iterations of
updates, and ends with a comparison of automated deployment techniques. The
objective is, on the one hand, to compare the advantages and disadvantages of
various technologies in theory and practice, so as to facilitate later adopters
to avoid making the generalized mistakes when implementing actual use cases,
and thereby choose a better strategy for their own enterprises. On the other
hand, to raise awareness of the evaluation framework for the deployment of
machine learning systems, to have more comprehensive and useful evaluation
metrics (e.g. table 2), rather than only focusing on a single factor (e.g.
company cost). This is especially important for decision-makers in the
industry.
