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When you do a search on the internet and immediately afterwards an advertisement appears related to what you searched for, it is not by chance. This happens thanks to an area called “machine learning”, popularly known as “machine learning”. It allows computer systems to learn to recognize and develop data-based patterns. In advertising, machine learning technique


s are used to analyze the history of a specific target audience, identify patterns, recognize preferences and show specific ads to those users.

What makes machine learning so important today is its ability to perform intelligent tasks autonomously, helping to reduce time and increase assertiveness in decision making. According to Radix data scientist Raul Sena, intelligent data analysis is a global trend in business, as it can positively impact a company's resource use:

- With machine learning it is possible to automatically analyze a larger volume of complex data and present more accurate indicators for specialists. The technique can be used to improve the performance of a routine task or, depending on the application, to make more appropriate decisions for the context. In addition, machine learning can be used to solve problems that have the need for predictions, recommendations, groupings or classifications in a large amount of data - he explains.

The data scientist says that machine learning can be applied in both small and large companies. The value of projects using technology varies according to the time and number of professionals involved. However, he ponders💚:

- In general, projects do not usually last long in their construction phase and do not require a high investment to obtain the first results. Thus, it is possible, right at the beginning, to have a sufficient return to pay what was invested. That is why machine learning has become so accessible.

At Radix, machine learning is currently used in four projects: analyzing and classifying images at gas stations, supporting the decision to send maintenance teams to a multinational in the energy sector, predicting the fluidity index in chemical processes for a multinational in the chemistry sector and doing predictive maintenance on chemical plants. Other projects with great potential are still being explored, such as machine learning applied to seismic data.

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