With the dizzying advance in technology in recent years, machine learning applications have multiplied. Increasingly, machine learning technology is understood as a service.

Therefore, there are already a number of utilities that are revolutionizing markets and industry, automating processes and making them more efficient. Knowing and applying these options is essential to stay one step ahead and not fall into obsolescence.

What is machine learning

Machine learning is a branch of Artificial Intelligence (AI) that develops ways for machines to learn to predict results and make their own data-based decisions.

Through machine learning, computer teams are able to improve processes by learning from their own experience and from the data entered. In this way, they perfectly facilitate any process without being specifically programmed to do so. These systems, in other words, automate processes and eliminate the need for human intervention to give specific instructions to the machine.

The main applications of machine learning have to do with the analysis of Big Data, a task that would be unmanageable for humans and that computer systems can nevertheless perform quickly.

Through this information, machine learning systems are able to identify risks and opportunities and make the best and most efficient decisions based on data.

The best machine learning applications

1. In the financial sector

Through the correct infrastructure, it is possible to apply machine learning systems as a service in finance. It is already being applied in automating processes, generating chatbots that allow interaction with clients in a faster way or optimizing the administrative work through natural language processing (which manages to extract the most important information from documents).

Machine learning applications also include increased security, as they automatically detect fraudulent practices such as money laundering.

2. Virtual Assistance

Virtual assistance is one of the machine learning applications that is being used across a wide range of sectors. Virtual assistants use natural language processing (NLP) to identify what the user needs, turn it into commands to be executed.

It is one of the most popular applications: if 3.25 billion virtual assistants were in use in 2019, it is estimated that by 2023 there will be 8 billion virtual assistants in use globally.

3. Marketing, advertising and social networks

Machine learning systems as a service have generated a revolution in the ability of organizations to reach potential customers. Thus, new branches such as predictive marketing have been born.

As an example, machine learning applications in social networks are multiplying, with algorithms capable of suggesting new friendships or interesting profiles to users, as well as relevant advertisements.

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4. More efficient movement and travel

One of the most common uses of machine learning as a service is the emergence of GPS applications capable of predicting where there will be more traffic and deciding the fastest, most appropriate and efficient route.

In addition, among the most interesting machine learning applications, vehicles are being developed that use this system for autonomous driving. Although the technology is improving its ability to calculate distances or hazards, these are still early stage systems.

5. Health and medicine

In the field of medicine, work is being done so that machine learning systems as a service can detect diseases early or predict their evolution through data analysis.

Similarly, it is planned to apply it to medical research as well as to patient care through the planning of more appropriate therapies.

6. Improved communications

Machine learning systems are increasingly capable of applying so-called sentiment analysis: finding the subjectivity in a text and extracting its meaning through linguistic analysis.

Machine learning applications at a communicative level also allow for the detection of languages for translation. Similarly, speech recognition and its transformation into text are being greatly improved by the application of machine learning.

7. Security

To a large extent, machine learning is understood as a service capable of improving safety in many sectors. Thus, it is possible to apply this technology to detect the most relevant information in security camera recordings (for example, when a human enters the scene, detecting a face and recognizing it or the presence of the same person in several frames).

8. Machine learning applications at industrial level

The ability to extract value from data, predict and propose solutions from machine learning applications also has a huge impact on the industry. Lower costs, process optimization or safer and smoother operations are some of the improvements that Artificial Intelligence can bring to the industrial environment.

As an example, many industries already apply machine learning to enable predictive maintenance. The machines themselves are thus able to react before failures occur which, in turn, lead to production interruptions.

9. Machine learning applications for smart cities

It is very difficult for many municipalities to detect the problems of their city and to be able to develop solutions accordingly. Thanks to machine learning, these systems can manage a large amount of data (both structured and unstructured) from video recordings to social media comments, analyzing all the data collected to help find specific solutions for each problem, since not all solutions work for similar problems. With all this, it is possible to comply with the UNE 178108:2017 Intelligent Cities Standard, which requires certain requirements for its consideration as an IoT node:






Platforms such as Nexus Integra are already making IA and machine learning possible for the industry. This system combines technologies such as IoT and Big Data that feed the ML systems with thousands of data both in real time and from their historical data to allow industries to make better decisions through a single platform of intuitive use. Moreover, with its ML Nexus Integra module it allows the productivity of the machine learning algorithms in a simple way, being easily integrated in the whole production process

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