Issue link: https://www.e-digitaleditions.com/i/1081695
March/April 2019 I 45 FIGURE 5: SDC extends the principles of SDN's primary focus on Ethernet and packet layers to the optical transport layer. SDC is a key enabler of automation throughout the network and across all operational levels and accelerates time-to- revenue from a month to minutes among other benefits. TABLE 1: Shows how the 7 layers of the ISO-OSI model map to the multiple layers of any enterprise AI system. 13 spectrum waste due to guard bands while facilitating seamless capacity growth without network re-engineering or major disruption to existing operating processes. Another interrelated innovation model designed to retire current methods of optical capacity planning, engineering, hardware- based deployments requiring numerous truck rolls, extensive manual labor, and human intervention involves leveraging software defined capacity (SDC) as shown in Figure 5. These frameworks can be considered the key building blocks toward the creation and deployment of cognitive networks. Sophisticated AI algorithms can be used, for example, to build a microservices- based path computation engine (PCE) that can replace manual offline route and capacity processes and can ultimately overcome multiple and often challenging optical fiber impairments. The models presented by Masoud are some of many as IoT and 5G continue to evolve. For example, Tapati Bandopadhyay offers another OSI model architecture integrating AI (see Table 1). It is advisable for ICT designers and professionals to embark on becoming more acquainted with various new architectures from IT, especially those that advocate increased collaboration between ICT, OT, and IT. API Layer: Containerized, easy to consume, easy to train and build on, easy to deploy, APIfied, modularized, AI in Lego forms. Presentation Layer: UI/ UX of AI use cases: Presentation and visualization of output such as classification models/clusters/ anomalies | Chat- text, voice, video | AR/VR | HMI Use-case Layer: Combining/ leveraging multiple core algorithms to solve business use-cases: e.g. Tensorflow API-> image process- ing-> image classification/ clustering -> damaged cars vs. normal cars basis whatsapp/ mobile images from field- for automotive insurance- remote damage evaluation Algorithms Layer: Selection of most apt algorithms basis the use-case/ problem [e.g. image/ text/ missed, temporal, transfer learning, reinforcement, NN's with memory- LSTM, HTM | Problems: classifier, clustering, fraud, profiling, CLV, churn, predictor, synthetic data gen, approximation, autoML, meta-learning Data Processing Layer: Data prep: quality checks, fitment for ML/training, assumptions testing, cleaning, sparse/lossy/noisy/blurred data handling, Data security and governance, privacy, access control, regulatory compliance [e.g. PII, GDPR] Data Integration Layer: Data search, identification of relevant & trusted sources, integration, data lakes, connectors, mixed data [structured- unstructured] Physical Layer: AI-Optimized Chips | Infra: GPU, TPU, Neuromorphic, Optical, Quantum computing 7-LAYER ENTERPRISE AI SYSTEMS ARCHITECTURE