But do you understand the difference between them and the gains Machine Learning delivers through data automation? Keep reading to find out more.
How does Machine Learning work?
The traditional way of working with data is based on rules predefined by programmers. In other words, rules are created that define how data will be interpreted and used in a given context.
In a company that sells products online, for example, the rules may determine that a customer's credit limit is X reais. Machine learning, however, works differently.
It uses complex algorithms that analyze the available data and learn from it. This allows the machine to establish patterns and create rules for interpreting data autonomously, offering different credit options to each customer according to their consumer profile, for example.
Characteristics of Machine Learning
Building machine learning analytical models is directly tied to Artificial Intelligence, which has gained a great deal of prominence in recent years, and whose main advantage is the ability to adapt to new data, something the traditional model cannot do as efficiently.
In addition, machines can analyze large amounts of data in a short period of time, which can lead to important discoveries and valuable insights for the company.
However, it is important to remember that Machine Learning does not completely replace the traditional model. In some cases, rules predetermined by programmers are necessary to ensure data integrity and security.
Machine Learning vs. the traditional model
The difference between the traditional way of working with data and machine learning, therefore, is that the former is based on pre-established rules, while the latter uses algorithms to learn and create data interpretation rules autonomously.
Thus, both have their advantages and disadvantages, and it is up to technology professionals to assess which model is best suited to the product being developed.
Enacom applies these concepts constantly, so that we can build innovative solutions side by side with our partners that deliver real, measurable results.
Advantages of Machine Learning
- Improved efficiency of production processes. with machine learning, production processes can be analyzed and monitored in real time, allowing settings and adjustments to be made more precisely and quickly for a significant improvement in production efficiency.
- Cost reduction. with the automation of decision processes and real-time data analysis, it is possible to reduce production costs by increasing energy efficiency and reducing waste.
- Improved product quality. with real-time data analysis, it is possible to identify quality problems faster and act to correct them, leading to improved product quality.
- Preventive maintenance forecasting. machine learning enables real-time data analysis so that equipment can be monitored continuously. This makes it possible to predict the need for preventive maintenance, helping reduce corrective maintenance costs and minimize production interruptions.
- Custom product design. using machine learning algorithms, it is possible to analyze customer data and predict which custom products or services will be most appealing to them. This can lead to increased sales, in addition to allowing companies to offer solutions better suited to customer needs.
How Machine Learning learns, in 5 techniques:
- Supervised learning. this is a type of machine learning technique that uses labeled data to train the model. The model is fed input data and the desired output and, from that, is able to predict the output for new input data.
- Unsupervised learning. unlike supervised learning, this machine learning technique does not use labeled data to train the model. It is based on discovering hidden patterns in the data that can provide valuable insights for the business.
- Reinforcement learning. this technique is often used for games and simulations, and consists of teaching the machine in a way similar to how a child learns to ride a bicycle. The model is programmed to receive positive or negative feedback as it performs its task and, from that, is able to learn the best approach to obtain the greatest amount of positive feedback.
- Semi-supervised learning. this technique uses a combination of labeled and unlabeled data to train the model. It is a good option for large datasets, since it allows the model to learn from a smaller number of labeled examples while using the unlabeled examples to discover hidden patterns in the data.
- Neural networks. this machine learning technique mimics the functioning of the human brain, with a network of artificial “neurons” communicating with one another. Neural networks are able to learn patterns in large unstructured datasets, such as images, audio, and text.
To understand better:
Labeled data is data that has a tag or label identifying the class or category it belongs to. These labels are provided by humans or by algorithms that classify the data. For example, in a dataset of animal images, each image may be labeled as “dog,” “cat,” or “bird.”
Unlabeled data, on the other hand, has no tags or labels identifying its class or category. This data is simply a set of information from which one can try to extract some knowledge or pattern. For example, in a dataset of online comment texts, the unlabeled data would be the text of the comments without any additional information, such as the rating of the product or service. This type of data requires more effort to obtain useful and accurate insights.
Enacom develops Machine Learning solutions for the energy, logistics, steel, healthcare, and sustainability sectors, and this technology tends to bring increasingly significant returns to industry.
Would you like to apply artificial intelligence capabilities to your company's production processes? Talk to our experts, we can help you!
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