an make that recom- mendation more intelligent based on data, context, previous patterns, and outcomes. Moreover, as the system continues to iterate and more data are collected, ML and AI can learn from successes and failures, and adjust their models accordingly to minimize errors. ML and AI help to transform the system from being static to being dynamic and from being reactive to being proactive. This is depicted in Figure 8.15. Network Data and Optimization Collect Predict Machine Learning Interpret Artificial Intelligence FIGURE 8.15 ML and AI for control-loop automation. TABLE 8.6 Predicting Network Failure T Times in Advance Predicting *T* Time Instances before the Event Happens. Values of T Below Accuracy (%) Note: Every time instance is 5 min for this data. The following is a preliminary experiment of using ML for predicting network failures in a closed-loop system [Rag16]. The goal is to predict as accurately as possible a network failure well in advance, that is, we are interested in predicting the occurrence of a network failure T time instances before the event has occurred. Around 50 signals were used that provide KPIs of a network perfor- mance. The failure translates to a CPU overload and system shut down (Table 8.6). 8.3.6 DEEP LEARNING AND SDN In the Network Cloud, deep learning can be applied for classifying network data for identifying security threats or detecting performance degradations of virtual functions. For example, one can capture memory and CPU signals from a virtual machine supporting a virtual firewall to moni- tor the health check of the firewall. A set of deep learners can be trained to predict the health of this firewall over a set period of time. Detected failures can then be addressed by either auto- matically rebooting the firewall, migrating traffic to another firewall, or issuing a ticket for manual intervention. The same concept of deep learning can be applied for interpretation when dealing with control loop systems. A deep learner can be trained to map analytics signals an