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Database Management and Machine Learning

A database is computerized system of information with the ability to search and process data (Vermaat et al. 556). Data is the collection of texts, images, audio, video, and other items presented in the form of records in the database.

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Data along with a database management system and applications are called a database system. Data is usually formatted in tables to ensure efficient processing and querying (Vermaat et al. 556). Therefore, it can be easily obtained, controlled, changed, monitored, and organized.

Self-managed are the latest and most revolutionary cloud-based databases that implement machine learning to robotize configuration, protection, backups, upgrades, and other common maintenance tasks. A data management system helps enterprises to leverage data from diverse sources by seamlessly integrating on-premises and cloud environments.

Machine learning is used in science, business, industry, healthcare, education, etc. The possibilities of using machine learning technologies are constantly expanding (Alpaydin 4). In business, machine learning helps to improve user experience, predict customer behavior, show relevant ads, customize personalized email campaigns, reduce the processing time for the request, etc.

Machine learning helps a business to analyze the behavioral factors of customers to maintain a competitive advantage over other businesses (Alpaydin 4).

With the full range of information about the target audience and existing customers, companies can improve the quality of communication.

In-depth analysis and trend detection help a business to stay ahead of the competition by being the first to offer the best solution to the customers.

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Data analysis helps to make e-commerce more profitable since it allows businesses to optimally use the capital and funds of the company, reduce risks, increase market stability, and efficiency.

Web analysts optimize marketing campaigns through robot assistants that recognize language and conduct a dialogue (Alpaydin 19). Personalized email campaigns and calls make potential clients more loyal.

Machine learning allows conducting a comprehensive analysis of information about potential suppliers and partners. This data can be subjected to careful analysis to build a rating of the reliability of counterparties.

To begin with, the task itself may contain no ethical intentions. For example, if machine learning is used to teach an army of drones to kill people, the results may be unexpected.

Not all algorithms are ethical; many of them work for the good of their creators. For example, in medicine, machine learning can be implemented to offer target users more expensive treatment.

Machine learning algorithms manipulate Internet users in various ways. The system advises which movie or news to watch, or which products to buy based on a person’s tastes (Alpaydin 11). This process violates privacy and changes tastes over time, making them narrower.

Many of the mechanisms using which modern systems process data are unclear to the developers. This casts a shadow on the safety of the result of the work of any smart machine. Therefore, AI algorithms should initially be designed in such a way that the actions of the system are safe and predictable (Alpaydin 14). When implementing machine learning, it is important not to collect as much data as possible, but to understand how to properly structure and process it so that automated protection tools work effectively.

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Machine learning can improve decision-making among management using in-depth analysis. It is possible to identify and predict the further development of events in many areas, as well as fill gaps in past observations. Algorithms assist in decision-making, continuously selecting the best parameters for any process.

Works Cited

Alpaydin, Ethem. Introduction to Machine Learning. MIT Press, 2020.

Vermaat, Misty E., et al. Discovering Computers 2016: Tools, Apps, Devices, and the Impact of Technology. Cengage Learning, 2017.

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