Diffusion Model of Anti-Patterns for Machine Learning Projects

The development of service systems leveraging Machine Learning (ML) has been actively pursued in recent years. In the development of general service systems, knowledge for effectively managing projects has been systematized. Similarly, in ML service system development projects (ML projects), knowledge such as best practices and patterns is being established. Among these, anti-patterns documenting situations that cause issues in ML projects and their corresponding solutions, are also being organized. However, knowledge like patterns and anti-patterns does not clearly define who should recognize their necessity or apply the solutions. As a result, in ML projects, where collaboration among stakeholders is critical, the utilization of such knowledge often depends on the experience and skills of the individuals involved. In this study, we propose a diffusion model for anti-patterns in ML projects and a method for representing the model. Furthermore, by representing actual anti-patterns as a diffusion model using the proposed method, we verify the effectiveness of the represented model.