What is Model Training?
Model training is the active, computationally intensive phase of AI development where a machine learning algorithm ingests a dataset, discovers underlying patterns, and incrementally adjusts its internal mathematics (parameters) to minimize errors.
How does it work?
Training is an iterative process. The model makes a prediction on the data, evaluates how wrong the prediction was using a "loss function," and mathematically updates its weights to be slightly less wrong the next time. This cycle repeats millions or billions of times across clusters of powerful GPUs.
What is it commonly confused with?
Model training is often confused with inference. Training is the phase where the model learns how to do a task. Inference is the phase where the deployed, finished model actually executes the task for a user.
Why does it matter?
Training a frontier foundation model can take months and cost hundreds of millions of dollars in compute resources. The decisions made before training begins—such as the quality of the dataset, the architecture of the algorithm, and the size of the model—cannot be easily changed once training starts, making it the most critical phase of AI development.