What it is
Model training is the fundamental phase in developing an artificial intelligence model, particularly in machine learning. During training, the model is exposed to a large dataset, often labeled, and adjusts its internal parameters to minimize errors between its predictions and the actual outcomes. This iterative process, typically involving algorithms like gradient descent, allows the model to learn complex functions and generalize from the training data, preparing it for tasks like classification, prediction, or generation.
Model training is a resource-intensive activity, demanding significant compute power, especially GPUs, and large, high-quality datasets. The cost and duration of training large models are major barriers to entry for AI development. Developments in training efficiency, new architectures, and data synthesis techniques are closely watched by investors as they impact the competitive landscape, the valuation of AI startups, and the demand for specialized hardware and cloud services.
Why it matters
Model training is the most expensive and compute-intensive part of AI development, driving demand for specialized hardware and impacting AI company valuations.
Reviewed under editorial standardsUpdated September 26, 2026Not investment advice