Continual learning on deployment pipelines for Machine Learning Systems

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 system is becoming a critical 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 pros and cons of various technologies in theory and practice so as to facilitate later adopters to avoid making generalized mistakes when implementing actual use cases and thereby choose a better strategy for their own enterprises. On the other hand, to raise the awareness of the evaluation framework for the deployment of machine learning systems, to have more comprehensive and valuable evaluation metrics rather than only focusing on a single factor (e.g., company's cost). This is especially important for decision-makers in the industry.