Technology

Model Training

The iterative process of feeding massive, labeled datasets to a machine learning algorithm to adjust its internal parameters (weights and biases) and minimize the loss function.

Model Training is the critical phase in the AI lifecycle: it’s where a selected algorithm (e.g., a deep neural network) learns from a high-quality, prepared dataset. The process is iterative: the model makes a prediction, calculates the error using a loss function (like Cross-Entropy), and then uses an optimizer (like Gradient Descent or Adam) to adjust its parameters across numerous epochs. This systematic error minimization, often involving billions of data points, teaches the model to recognize complex patterns. The objective is simple: ensure the final, trained model generalizes effectively to make accurate, real-world predictions on new, unseen data, powering applications from fraud detection to generative AI (e.g., training a BERT or ResNet model).