This presentation delves into advanced machine learning techniques for evaluating and predicting the quality of microservice architectures (MSA). It starts with an overview of learning-based evaluation methods, emphasizing the role of deep learning and transfer learning in quality assessment. The discussion extends to the use of Generative Adversarial Networks (GANs) and data augmentation techniques to improve model robustness and accuracy. The presentation highlights the application of these advanced techniques in predicting the quality of MSA, culminating in the introduction of a novel method that leverages these technologies to provide more accurate and reliable quality predictions. | 
