AI model training is the process of teaching a machine learning model to perform a specific task by exposing it to data and allowing it to learn patterns and make predictions.

Here's a more detailed breakdown of the process:

• Data Preparation: Gathering, cleaning, and organizing data that is relevant to the task the model needs to learn.
• Evaluation: Assessing the model's performance on unseen data to ensure it generalizes well.
• Iteration: Repeating the training and evaluation steps to improve the model's accuracy and performance.

At its core, an AI model is both a set of selected algorithms and the data used to train those algorithms so that they can make the most accurate predictions. In some cases, a simple model uses only a single algorithm, so the two terms may overlap, but the model itself is the output after training.

In a mathematical sense, an algorithm can be considered an equation with undefined coefficients. The model comes together when the selected algorithms digest data sets to determine what coefficient values fit best, thus creating a model for predictions. The term “AI model training” refers to this process: feeding the algorithm data, examining the results, and tweaking the model output to increase accuracy and efficacy. To do this, algorithms need massive amounts of data that capture the full range of incoming data.