 applica- tions and services. Over the past three decades, ML technologies have shown to be superior for applications includ- ing object detection, speech recognition, and NLP. In speech recognition, ML models, known as deep learning, are used to estimate the posterior probabilities of context-dependent linguistic units, such as phonemes and words [Den13]. Those probabilities are applied in a Markov hidden model to discover the best sequence of units. Training these ML models on a large variety and volume of data helps to make speech recognition systems speaker independent and robust to adverse environ- ments and conditions. A form of ML, known as artificial neural network, has been used to estimate the articulatory parameters of a vocal tract model for generating synthesized speech [Rah91]. Other types of ML models, known as convolutional neural networks [Lec98], have been used to spot Network Data and Optimization objects (e.g., characters, cars, dress, shoes) in an image enabling businesses to perform targeted advertising. ML has also been used to predict network failures. This proactive action can result in increased cost saving and improved customer experience [Gil16]. These successful demonstrations of ML applications continue to raise interest in academia and industry, a precursor for enabling smarter automated systems. 8.2.5.1 Descriptive versus Reactive versus Predictive versus Prescriptive Analytics There are different forms of analytics that vary in their level of sophistication. Descriptive analyt- ics is the simplest form of analytics that involves summarizing the data to generate a report. This is very commonly used today in commercial applications. Reporting can be in the form of tables or more sophisticated visualization methods. Descriptive analytics provides a useful aggregation of key performance indicators (KPIs) to monitor a system or a service. Reactive analytics is also a commonly used approach in commercial applications. It involves raising an alarm based on processing data and events; rea