How to train an AI model
Training an artificial intelligence (AI) model is a complex process that involves several steps. Below we provide an overview of how an AI model is typically trained using a machine learning-based approach:
1. Definition of the problem
- Clearly identify the problem you want to solve (for example, classification, regression, clustering, etc.).
- Determine the evaluation metrics that will be used to measure the performance of the model.
2. Data collection (data lake)
- Gather a sufficiently large and representative data set that contains examples of the task to be learned.
3. Data preprocessing
- Clean the data by removing or treating missing values and errors.
- Transform the data into a format suitable for the selected algorithm (eg normalization, category coding, etc.).
4. data division
- Split the data into training, validation (optional), and test sets. Training is performed on the training set, validation on hyperparameter tuning, and final evaluation on the test set.
5. Model selection and algorithm
- Choose an appropriate model and algorithm for the problem, such as a neural network, support vector machine, decision tree, etc.
6. Hyperparameter configuration
- Configure model parameters, such as the learning rate, the number of layers in a neural network, etc. This may require experimentation and adjustment.
7. Model Training
- Feed the training set to the algorithm, allowing the model to fit its internal parameters to minimize a loss function.
- Use techniques such as gradient descent to optimize model parameters.
- Monitor the training process to avoid problems such as overfitting, where the model performs well on the training data but poorly on unseen data.
8. Validation and adjustment
- Evaluate the model on a validation set to refine the hyperparameters and select the best version of the model.
9. Evaluation
- Test the model on a separate test dataset to assess its performance under conditions similar to what it will face in production.
10. Implementation and monitoring
- Deploy the model in a production environment.
- Monitor your performance in real time, possibly adjusting and retraining as needed.
Training an AI model can be an iterative and highly experimental process, where each stage may require adjustments and decisions based on domain knowledge, data analysis, and understanding of algorithms. Modern machine learning tools and platforms offer a wide range of functionalities that facilitate this process, but, even so, effective training of an AI model requires a combination of technical skills and knowledge in data sciences, statistics, computer science... , and even the knowledge of the business or field of application itself.
See also: Steps in the implementation of an artificial intelligence office. Where to start?
1MillionBot develops and trains in the CSB an LLM model.