AI Models: cómo entrenar, validar, ajustar e implementar

IA Models: how to train, validate, fine-tune, and deploy

The entrenamiento de modelos IA is one of the fundamental pillars to leverage artificial intelligence in the business environment. Thanks to neural networks, which mimic the functioning of the human brain, your business can make much more use of data, obtaining fully customized tools based on the unique information of the company. But, what is the process to achieve this utilization of artificial intelligence? What tools does your organization need?
In the following sections, we will explain why artificial intelligence has become so important in business environments. Additionally, we will discuss some basic concepts related to this technology and the process involved in creating completely unique AI models, powered by your business data. We welcome you to Industry 4.0, focusing on one of its key components: AI.

IA models, artificial intelligence, and businesses

Artificial intelligence is making its way into the business world. And it is doing so in leaps and bounds. Why are we witnessing this phenomenon? Well, the first thing to make clear is that AI is not a new proposal. Beyond generative artificial intelligence (GenAI), which aims to generate content, algorithms and neural networks have been used for years to detect patterns, perform repetitive tasks, and make low-level decisions automatically.
For an AI to function, whether to create texts, like ChatGPT, or to execute tasks on its own, it needs to be fed with data. This is where modelos IA come into play. We are talking about a system designed to perform specific tasks using machine learning algorithms. These algorithms allow the model to learn patterns and make decisions based on data.
Currently, we can discriminate between different types of learning in IA:

• Supervised learning. The model is trained with labeled data (known as training data), allowing it to make accurate predictions. The data is generally curated by humans, which is why the term supervised is used.
• Unsupervised learning. It is based on unlabeled data. Basically, information of all kinds is provided to the algorithm so that it learns from it, delegating to the neural network the ability to label this data.
• Reinforcement learning. It is a system where the model learns to make decisions through trials that have positive or negative outcomes. It is the human who decides whether the results are good or not, and based on this decision, the machine refines its behavior.

The IA models, in addition, are trained using three different datasets. They are as follows:

• Conjunto de entrenamiento. It is the data package used to train the model. It can be likened to an instruction manual with which the AI model learns to perform its tasks.
• Validation set. These data are what allow the adjustment of the hiperparámetros, that is, the more specific values that configure the behavior of the model. This way, overfitting is avoided, which is when the model strictly adheres to what it has learned, losing the ability to incorporate new information and contextualize the data.
• Conjunto de pruebas. It is the set responsible for evaluating the final performance of the model to ensure it works correctly with new data. It allows us to know if the AI model is capable of extrapolating its initial knowledge to new sets provided later.

Everything analyzed seems complex, but when put into practice, it is quite simple. In reality, it is similar to training an employee you have just hired. The only difference is that this one will be responsible for making low-level decisions and executing the most repetitive tasks automatically, freeing real employees from these responsibilities and improving their productivity.

The complete process for training AI models

Now that you know more in-depth how AI models work, we want to talk to you about the process that must be followed to train them. Let's look at the exact steps that allow training an artificial intelligence so that it can develop functions and execute tasks within a company's operations.
Here is a brief explanation of the steps to train an AI model, with additional terms included:

Data collection

First, relevant data for the task to be solved is collected. It is important that the data is representative and of good quality, even if it comes from various sources. Machine learning libraries and AI frameworks are often used to manage and prepare this data.

Algorithm selection and parameterization

Once the data is available, the next step is to choose the machine learning algorithm, based on the type of task to be covered. Then, hyperparameters are set, which are initial configurations of the model that can affect its performance. During this phase, regularization techniques can be applied to avoid overfitting.

Training

The training set is used to teach the model. The model learns as its internal parameters are adjusted to minimize errors in its predictions on this data. This is a stage where AI frameworks are often used.

Validation

The model is evaluated using the validation set. This process is part of model validation, and performance metrics are used to measure its effectiveness. This is when it is discovered if the AI model will be able to handle new data and if it has not become too specific.

Ajuste de modelos IA

Basándose en los resultados de la validación, se ajustan los hiperparámetros y el modelo. Este proceso de ajuste de modelos puede repetirse varias veces para optimizar el rendimiento del modelo.

Implementación

Una vez que el modelo está bien entrenado y validado, se despliega en el entorno real donde hará sus predicciones o tomará decisiones basadas en nuevos datos. Este proceso se conoce como implementación de modelos. Después, se realiza el monitoreo de modelos y la actualización de modelos cuando sea necesario.

Discover how to create AI models with WatsonX

The WatsonX platform from IBM is a comprehensive tool developed by the company to enable businesses to implement the various functionalities offered by artificial intelligence.
Within the different components of WatsonX, we find Watsonx.ai, an all-in-one solution that allows you to train, validate, adjust, and implement AI models using generative AI, foundational models, and machine learning. The main benefits are:
It offers a secure study environment.

• The tools are intuitive and easy to handle.
• Possibility to use both custom foundational models and IBM models.
• Uses open-source tools to facilitate programming, automation, and data visualization.
• Employs advanced AI models that work correctly even with little information.

In addition to this cutting-edge platform, we provide you with the data analytics division of SEIDOR, the reliable partner your company needs for the implementation of these solutions. Contact us now and unlock the potential of AI models in your company!