Model

Full definition

A model is what you ship. It encodes everything the algorithm learned from the data into a fixed file (often gigabytes to terabytes for modern frontier models). The same algorithm trained on different data produces different models; the same data run through different algorithms also produces different models. Governance regimes such as the EU AI Act regulate models and their deployment context, not algorithms in the abstract.

Why it matters

Knowing exactly which model is in production — version, training data cutoff, evaluation results — is the prerequisite for debugging, audit, regulatory response, and rollback. Organizations that cannot answer "which model is running where?" cannot meaningfully govern AI.

Example

GPT-4o, Claude Sonnet 4.5, and Gemini 1.5 Pro are specific models. Each is a frozen artifact with a published model card describing training, evaluation, and limitations.

Next steps

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Related terms

• Algorithm
  An algorithm is a finite, well-defined sequence of computational steps that takes an input and produces an output, designed to solve a class of problems — for example, sorting a list, ranking search results, or training a neural network.
• Foundation Model
  A foundation model is a large model trained on broad data at scale — typically self-supervised — that can be adapted to many downstream tasks via prompting, fine-tuning, or retrieval, instead of being trained task-by-task.
• Large Language Model (LLM)
  A Large Language Model (LLM) is a deep neural network — almost always a transformer — trained on hundreds of billions to trillions of words to predict the next token, and to generate, summarize, translate, or reason over text.
• Fine-Tuning
  Fine-tuning is the process of further training a pretrained model on a smaller, task-specific dataset so it specializes in a particular style, domain, or behavior — without retraining from scratch.
• MLOps
  MLOps is the discipline of operating machine-learning and AI systems reliably in production — covering data pipelines, model training, deployment, monitoring, drift detection, governance, and incident response — analogous to DevOps for traditional software.

More fundamentals terms

• Supervised Learning
  Supervised learning is the machine-learning paradigm in which a model learns from training examples paired with correct labels, then predicts labels for new, unseen inputs — the dominant approach in production ML today.
• Unsupervised Learning
  Unsupervised learning is a machine-learning paradigm where a model is given only inputs — no labels — and must discover structure in the data: clusters, density, low-dimensional representations, or anomalies.
• Artificial Intelligence (AI)
  Artificial Intelligence (AI) is the field of computer science focused on building systems that perform tasks typically requiring human intelligence — including learning from data, reasoning under uncertainty, recognizing patterns, understanding language, and making decisions.