What it takes to Train an AI Model

Training a large language model is a vital preparatory stage in enabling AI. It ensures that the model learns from suitably labelled data, which in turn enables it to accurately make predictions or classifications. So, let’s have a look at the main elements of that process as we will probably be involved at least with some of them sooner or later. (It also helps to dispel the myth of machines thinking and taking over the world).

Data Collection

The first step is to gather and prepare a high-quality dataset that represents the problem you want to solve. This dataset typically includes input features (variables) and target labels (the values you want the model to predict or classify).

Data Preprocessing

Before feeding the data into the model, it often needs to be cleaned and pre-processed. This can involve tasks such as handling missing values, normalizing, or scaling features, encoding categorical variables, and splitting the data into training, validation, and test sets etc. Quite a lot of work this!

Model Architecture

Choose the type of machine learning or deep learning model that is most appropriate for your problem. This includes selecting the architecture, layers, and activation functions for neural networks, or choosing algorithms and hyperparameters for other types of models (e.g., decision trees, support vector machines, etc.).

Loss Function

Define a loss function (also known as a cost or objective function) that quantifies how well the model's predictions match the actual target values. The choice of loss function depends on the type of problem (e.g., regression, classification etc.) and the nature of the data.

Optimization Algorithm

Select an optimization algorithm (e.g., gradient descent, Adam, RMSprop) to minimize the loss function and update the model's parameters during training. The choice of optimizer and its hyperparameters can affect the training process. This is really like a gauge of how quickly and accurately to learn.

Training Process

Train the model on the training dataset by iteratively adjusting the model's parameters to minimize the loss. This involves forward and backward passes through the network (for neural networks) and updating the model's weights. Training continues until a stopping criterion is met, such as a fixed number of epochs or convergence of the loss. Again, this can last as long as it takes.

Validation and Hyperparameter Tuning

Use a separate validation dataset to monitor the model's performance during training. Adjust hyperparameters (e.g., learning rate, batch size, model architecture) based on validation results to improve model performance. Not a straightforward process this, eh?

Evaluation

After training, evaluate the model's performance on an independent test dataset to assess its generalization to unseen data. Common evaluation metrics vary depending on the problem, such as accuracy, precision, recall, F1 score, for learning evaluation, for classification.

Regularization

Now we come to one of the most important aspects of machine learning. Regularisation means applying techniques which prevent overfitting, this ensures that the model performs well on the training data and also on unseen data.

Deployment

If the model meets performance requirements, then deploy it in a production environment to make predictions on new, real-world data. Deployment considerations may include latency, scalability, and maintaining model performance over time.

Monitoring and Maintenance

Continuously monitor the deployed model's performance and retrain it periodically with new data to ensure it remains accurate, performant, and up to date.

Interpretability and Explicability

Depending on the application, it may be essential to understand how the model makes predictions. Techniques like feature importance analysis or model-specific interpretability tools can help explain model decisions, which may also be adjusted over time.

What comes out of all of this is that training the LLM is by no means a straightforward, easy, simple, or quick process and certainly gives an insight into what it takes to train a model.