Model Components
Model
In machine learning, a model is a parameterized function learned from training data that can map new inputs to predictions or generated outputs. Models encapsulate patterns extracted from data in their weights and architecture.
Models vary enormously in complexity—from linear regression with two parameters to LLMs with hundreds of billions. Selecting the right model type for a task requires balancing accuracy, interpretability, compute cost, and data requirements.
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Related Terms
Core Concepts
Machine Learning
Getting computers to learn from data and improve at tasks without explicit programming.
Model Components
Model Architecture
The specific structure of an AI model: its layers, connections, and component design.
Techniques & Methods
Training
Teaching a model to make accurate predictions by exposing it to large datasets.
Techniques & Methods
Inference
Using a trained AI model to generate predictions or responses on new, unseen data.