Design of a Full-Process Transaction Monitoring and Risk Feedback System for DevOps Based on Microservices Architecture and Machine Learning Methods

In the current age, with industries undergoing digital transformation, there exists a significant issue regarding transaction systems that are characterized by high rates of concurrency and complexity. These systems impose considerable demands on how monitoring is conducted and how feedback regarding risk is communicated. Traditional mechanisms for monitoring are oftentimes struggling with processing large volume of transactional data in real time, and there is an urgent necessity to improve predictions related to risk accuracy. This paper brings forward a monitoring and risk feedback system that uses a full-process approach specifically designed for DevOps. This is based on a framework comprising of microservices architecture along with techniques rooted in machine learning. The main goal is to achieve improved service decoupling and flexible deployment by utilizing microservices architecture effectively. The accuracy of identifying risks and the efficiency of risk identification processes are meant to be enhanced through the application of machine learning algorithms. Moreover, continuous integration alongside continuous deployment and continuous monitoring of the system is intended to be ensured via practices affiliated with DevOps methodology. The system facilitates modular and scalable transaction monitoring, which supports handling high concurrency as well as low-latency processing of data. Finally, machine learning methods are employed to enable predictive measures regarding risks and facilitating timely feedback in the event of potential risks.