Artificial Intelligence and Machine Learning for EDGE Computing

Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Readers will learn about the benefits that will be drawn by the integration of these technologies: How are they going to integrate and what will be the challenges for this integration? The growing number of devices and applications in the diversified domains of industry, gaming, speech recognition, medical diagnostics, robotics and computer vision, to name a few, are driven by Big Data, Artificial Intelligence, Machine Learning, and distributed computing, may it be Cloud Computing, or the evolving Fog and Edge Computing paradigms. The challenges that are prominently encountered include: remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing explores the tools, techniques of communication, AI/ML algorithms, architectural evolution in distributed ecosystems that shall enhance the possibility of rendering predictions by storage and computing options close to the end nodes. Edge computing is a new area of focus for communication service providers, enterprises and technology vendors. In particular it has been viewed as the next area of innovation and a key component of any 5G and IoT strategy. The Edge holds a significant promise for service providers as a new business opportunity, and the emergence of AI has been surprising in its power to accelerate what is happening on the Edge. Machine Learning and Deep Learning have combined with increased computing power to make Edge devices extraordinarily smart — and getting smarter all the time. This combination of emerging technologies enables devices to provide insights and predictive analyses in real-time. Whether it means a small device on a lamp-post can now recognize a car that is speeding, who is in the car, and whether they have a license, or that a manufacturer can see hiccups in its supply chain and proactively avoid unplanned downtime. With on-device AI, reliability no longer depends on network availability or bandwidth, and data processing becomes instantaneous. Sophisticated Machine Learning models on the Edge will impact video frames, speech Provides a reference handbook describing the evolution of distributed systems, including Cloud, Fog, and Edge Computing. Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers. Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints, and the technological/architectural developments that shall overcome those constraints.