Abstract: In order to ensure the safety and efficient operation of cloud services, it is necessary to conduct comprehensive monitoring and analysis of various accessing behaviors and operations in cloud platforms, aiming at detecting potential abnormal behaviors in a timely and accurate manner. There are numerous anomaly detection tasks in cloud platforms. Existing approaches usually require task-specific customized design and fine-tuning, lacking generalizable model construction capabilities for various anomaly detection tasks. Moreover, this process heavily relies on machine learning expertise, making it challenging for domain experts to develop effective models suitable for practical applications. To address the above problems, an intelligent lightweight model construction solution is proposed for anomaly detection in cloud platforms. Compared with existing technology, the proposed solution 1) supports diverse types of anomaly detection tasks in cloud platforms, allowing domain experts to achieve a timely and self-service construction of task-oriented anomaly detection models by simply providing basic task-specific configuration information; 2) enables domain experts to intelligently construct lightweight models for target anomaly detection tasks through automated feature construction, feature optimization, and hyperparameter tuning, even with minimal machine learning and deep learning expertise. Both the case studies and the experimental results based on data collected from large-scale real-world cloud scenarios demonstrate the proposed solution could build models for relevant anomaly detection tasks in a timely and self-service manner, while strong detection capabilities are also achieved in the constructed models.