Security Issues and Privacy Concerns in Industry 4.0 Applications - Группа авторов

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      Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106

       Advances in Data Engineering and Machine Learning

       Series Editor: M. Niranjanamurthy, PhD, Juanying XIE, PhD, and Ramiz Aliguliyev, PhD

      Scope: Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise.

      It is important to have business goals in line when working with data, especially for companies that handle large and complex datasets and databases. Data Engineering Contains DevOps, Data Science, and Machine Learning Engineering. DevOps (development and operations) is an enterprise software development phrase used to mean a type of agile relationship between development and IT operations. The goal of DevOps is to change and improve the relationship by advocating better communication and collaboration between these two business units. Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

      Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction. Machine learning engineering is the process of using software engineering principles, and analytical and data science knowledge, and combining both of those in order to take an ML model that’s created and making it available for use by the product or the consumers. “Advances in Data Engineering and Machine Learning Engineering” will reach a wide audience including data scientists, engineers, industry, researchers and students working in the field of Data Engineering and Machine Learning Engineering.

      Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])

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