Inteligencia artificial para redes: ¿Qué es y por qué son tecnologías clave?

Artificial intelligence for networking: What is it, and why are they essential technologies?

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We may define Artificial Intelligence (AI) as software that performs tasks like a human expert in a related topic. That is why we use the word “artificial.” In IT, artificial intelligence for networking reduces complexity for the growth of such networks.

Administering IT infrastructure becomes more complex with the growing number of connected devices, data, and people. Additionally, most budgets for IT are either fixed or short. Thus, organizations call for a better way to assist this complexity, and to do so; they seek the aid of artificial intelligence for networking.

Essential AI technologies

Machine Learning (ML) is essential for a successful artificial intelligence for networking; it uses algorithms for data analysis, to learn from them, and to make predictions without the need for explicit instructions.

ML has evolved to more complex structured models thanks to improvements in processing and storage capacities. Thus, for example, Deep Learning (DL)uses neural networks to achieve greater valuable information and automation levels.

Another trend that has driven artificial intelligence development for networking is Natural Language Processing (NLP). NLP uses word-based voice recognition. This way makes communication between the human and the machine components easier through hints and natural language queries.

How to create artificial intelligence for networking?

IT teams need the right artificial intelligence for networking strategy to meet current network requirements. The following are some technology components necessary for a strategy.

  • Data:

Artificial intelligence for networking develops its intelligence through data gathering and analysis. As the amount of data gathered increases, the solution provided by AI becomes more intelligent. For real-time applications which call for highly distributed peripheral devices (such as IoT or mobile), gathering data from each device becomes necessary. This way, data are processed locally or close to a peripheral computer or cloud using AI algorithms.

  • Specific knowledge of the domain:

Artificial intelligence for networking calls for labeled data based on knowledge specific to each domain. They allow AI to break down the problem into smaller segments which may be used to train AI models.

  • Data science toolkit:

Once the problem has been broken down into smaller metadata segments specific for each domain, they are ready to enter the ML and Big Data world. ML techniques are necessary to analyze data and offer actionable information.

  • Virtual network assistant:

Collaborative filtering is an ML technique that may be applied to sort large data sets. Likewise, those which make up an artificial intelligence for networking solutions are identified and related to a particular problem.

The virtual network assistant can operate in a wireless environment as a virtual network expert that helps solve complex problems.  This is, it allows for automated improvements.

At NGIN Services, we offer solutions for your business, keeping development in mind and using artificial intelligence for networking. Contact us! Wait for part two of this topic and learn about the practical benefits of artificial intelligence for networking.