Causal modeling methods are gradually being introduced into the field of machine learning, among which models represented by dual learning strategies (DML) are widely used in data inference and prediction. However, the current DML lacks a mechanism to intelligently select the optimal learning algorithm based on sample features, which can easily lead to inference errors; On the other hand, traditional cloud service platforms based on IaaS resource scheduling methods are difficult to meet the requirements of efficient parallel computing, resulting in limited model deployment efficiency and performance. To this end, this article proposes a DML training and deployment system that integrates serverless technology, constructs an adaptive selection mechanism for learning methods based on sample attributes, and designs and builds an efficient and easily scalable λ—DML training platform by leveraging the advantages of call based billing and automatic expansion functions. The experimental evaluation results show that compared with traditional cloud infrastructure, this system not only performs better in service response speed and model accuracy, but also significantly reduces usage costs. This study demonstrates the potential of serverless computing in optimizing DML model training, providing a new technological path for the implementation of causal inference methods in cloud environments.