Artificial intelligence model training is a process in which an algorithm is taught to correctly interpret data and make accurate decisions based on that data, in order to solve certain tasks.
The success of this process depends primarily on the quality of the data selected and the control over each phase to be completed.
The process of learning algorithms can be quite challenging, so it is important to consider the basic steps and best practices required to successfully train an AI model.
Steps to follow in the training of artificial intelligence models
AI model training requires the execution of the following crucial stages:
1. Dataset preparation
In the pre-training phase, it is essential to collect real data and prepare them. Various collection methods exist: private collection, automated data collection, personalized collective collaboration, etc. The method is chosen based on the scope or objective of the project. For example, a computer vision model requires images of a certain size and quality.
To raise the quality and relevance of the selected dataset, it is necessary to clean and improve it (data pre-processing). Data modelling is then carried out, a phase in which the relationships, variables and constraints to be represented in the dataset are identified.
Then, the data must go through an annotation process (manually or automatically using an intelligent algorithm), which means labeling it so that it is easier to interpret for the machines. For example, in computer vision model training, images should be labeled.
2. Model selection
One of the most crucial decisions in training artificial intelligence models is selecting the appropriate architecture or algorithm that effectively addresses the target problem.
The types of AI models are diverse; there are neural networks, random forests, decision trees, etc. In order to opt for one or the other model, we must define:
The problem and its degree of complexity.
The structure and size of the available data.
The degree of accuracy desired.
The computational resources available.
For instance, when the objective is to detect atypical values within a dataset, an excellent choice would be an anomaly detection model. On the other hand, for image classification tasks, a convolutional neural network model is the best option.
3. Initial training
The initial training consists of entering the prepared data into the model, in order to detect the errors that may arise.
It is a phase in which, after the information has been entered, the model is asked to make certain decisions based on that data. This is the beginning of the learning, so the model can stumble, like a child learning to walk. All these stumbles are the errors that must be adjusted to make the model more accurate.
During this step, it is important to prevent overadjustment, that is, to prevent the model from specializing and learning only to solve certain conditions, becoming unable to generalize to adapt to new tasks.
4. Training validation
During the validation phase, all assumptions regarding the functioning of the model are confirmed and validated using a new dataset (validation data).
The results obtained are analyzed for deficiencies. Even if there is an overadjustment problem, it will become visible at this validation stage.
5. Model Testing
Testing is the final step of training artificial intelligence models. The data used in the test is from the real world, unstructured and unlabeled data.
If it produces accurate results, the model is ready for use.
If it does not offer the desired accuracy, the model must go through the training stage again.
Best practices to conduct successful AI training
Some of the best practices in the process of training artificial intelligence models are as follows:
Understand early on both the problem and the goals of the artificial intelligence / machine learning project.
Collect concise data and evaluate it to ensure quality and relevance.
Use correctly labeled The tags created for the data annotation phase should be specific enough to be useful but still general enough to cover all possible variations in the selected dataset.
Start training with a small dataset. Take a sample of the data to start adjusting and evaluating the results.
Have enough data. The higher the amount of data, the more accurate the results will be.
How to train a machine learning model
It is worth mentioning that machine learning is a subfield of artificial intelligence. Therefore, machine learning models are AI models, but AI models are not necessarily machine learning models.
If we take into account the main types of machine learning, the following forms of training are available:
Supervised machine learning algorithms. In this case, the training of the algorithm is done by a human; a data scientist expert in the task being taught to the model.
These models are commonly utilized for conducting predictive analyses, where the input data consists of expert decisions or previously obtained results to anticipate future behaviors.
Unsupervised machine learning algorithms. Training is carried out by software or training models that may or may not teach in the same way as a human being.
For example, these models can be trained for content classification or summarization and for identifying patterns.
Semi-supervised machine learning algorithms. In this model, the first part of the training is carried out by a human and the remaining part of the training is done by software or training models, based on the initial training that has been carried out by the human.
Pangeanic, a leader in AI model training
We can make your AI model smarter. Thanks to our repository of over 10 billion data segments, we deliver custom data collection to you in any language.
In addition, within our artificial intelligence model training services, we carry out data annotation, labeling them to identify their relevant characteristics, pattern recognition and response refinement.
At Pangeanic, we provide the data that grows your company. Contact us. Make the most of everything that AI can offer you.