Designing secure microservices
Using ML, we can design different intelligent, predictive services that may use one or more algorithms including Feed-Forward Neural Networks (FFNNs), Deep Belief Networks (DBNs) (https://www.sciencedirect.com/topics/engineering/deep-belief-network), and Recurrent Neural Networks (RNNs). To facilitate the reuse of customized algorithms, we may choose to create an abstraction layer and encapsulate each of the predictive services as data-oriented microservices that can be integrated with applications requiring ML capabilities. Further, one ML microservice may be trained using the TensorFlow library, another may use the PyTorch library, and a third microservice may be trained on the Caffe library. Microservice-based ML models allow maximum reuse of ML libraries, algorithm features, executables, and configurations, fostering collaboration among ML teams.
For example, as illustrated in Figure 4.10, there are four ML predictive microservices – Recommendations...