We will see what the terms artificial intelligence and machine learning have in common and what role these concepts have in the development of certain cloud computing processes.

Machine Learning (ML), is a branch of artificial intelligence that has the ability to identify computational patterns and repeat them automatically. 

Machine learning uses algorithms to identify patterns based on data. Thanks to machine learning, we enable innovation, certain processes can be automated and we enhance prediction. These are some of the characteristics of machine learning, which are not new to us and which many companies are already starting to use in the business environment.

Machine learning tries to copy certain actions performed by people.  Machines have to be able to identify, think and understand like a human. The process of machine learning is similar to data mining: both of them search through data to find patterns.

Therefore, machine learning is data-driven. From this data, models are pre-trained in order to identify patterns and use this knowledge to automate tasks so that machines can perform them on their own. 

Machine learning focuses on the development of computer programs that can change when exposed to new data. Within this branch, and especially depending on the data to which the systems are exposed, we can talk about supervised or unsupervised learning.

As we discussed in the previous point, the typology of algorithms depends on the data to which the systems are exposed. These are some of the different types of machine learning:

Supervised learning

This is one of the least complex types of machine learning. In this case it tries to replicate "labeling", where the system is fed a set of data with an input and an output that allows it to learn and replicate a human task. In essence, it tries to classify a set of categories based on previous models so that it can be replicated later.

Unsupervised learning

Unlike supervised learning, in this type of learning we don´t have data sets with an input and an output, we only have input data. It is a model that fits the observations. In principle, there is no prior knowledge as in supervised learning. In this case the system is able to learn, understand and identify unknown input patterns.

Semi-supervised learning

Semi-supervised learning is a hybrid of the previous two. This type of machine learning takes into account supervised data in order to interpret and understand unsupervised data. 

Reinforcement learning

This is observation-based learning. The system or algorithm tries to observe its surroundings. The system tries to learn based on trial and error, in other words, it learns based on positive or negative reinforcement in order to identify the best action and, therefore, minimize risk and make good decisions.

What are the main advantages of starting to implement machine learning models within our organization? Here you can see some of them:

  • Process automation: in the age of digital transformation, one of the main processes is automation. Systems must allow developers and IT teams to continue creating value and not waste time on certain processes that can be automated. 

  • Prediction: because ML serves to identify patterns and behaviors, we can move to a predictive environment where we improve our customer experiences. 

  • Information: by using data to identify patterns, this data provides us with information on the current status of our processes, detects errors, and can predict behaviors or the current status of the customer.

  • Cost reduction: thanks to the automation of certain processes and the fact that IT teams no longer waste time on maintenance, we reduce costs. But above all, we offer the necessary space for teams to spend more time innovating and generating value.

The advantages are clear, but let's look at some of the most common use cases to which the vast majority of sectors are already being subjected: 

  • Healthcare services: the healthcare sector is one of the most advanced in terms of digital transformation. And machine learning is one of its resources. There is a clear evolution in how machine learning helps healthcare workers in the diagnosis or supervision of patients through X-rays, monitoring, etc.

  • Retail: the retail sector is another sector that can benefit the most from machine learning. It can help marketing managers or product managers to optimize campaigns, offers, prices and use data to improve the overall customer experience.

  • Logistics: one of the biggest advantages for this sector is the improvement in the supply chain. We have said that machine learning allows us to move to a predictive environment. Thanks to IoT and Machine Learning, we will be able to predict the waiting time for the repair of a conveyor belt or the tracking and optimization of the delivery of products until they reach the end consumer.

Unlike conventional machine learning, this type of learning learns from much more complex data patterns. This type of learning has the ability to access a larger amount of data thanks to artificial neural networks. By creating computational models composed of several layers of processing, the networks can create several levels of abstraction that represent the data.

Another difference in deep learning is that the recognition of certain features is completely independent and autonomous. In contrast, in conventional learning, models have to be pre-trained even if they then start working on their own. Even so, certain parameters will always have to be preset for the system to be effective. 

After reviewing the characteristics, applications and use cases of machine learning in the business world, it is clear that it is a tool that greatly facilitates the development of certain processes. It allows us to move to prediction and offer better experiences to our users and customers, which is something very much in demand nowadays.