Definition
Model
In AI, the trained system that takes an input and produces an output, like a prediction, classification, or generated text.
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Related terms in AI Fundamentals
Loss functionThe formula that scores how wrong a model's predictions are. Training is the process of driving that score down.Machine learning (ML)The branch of AI where computers learn patterns from data and improve at a task without being explicitly programmed with rules.Markov chainA model of a sequence of events where the odds of what happens next depend only on the current state, not on how you got there.ML modelA custom machine learning model trained on your own data, as distinct from a general-purpose LLM. More expensive and data-hungry to build, but harder to replicate because the training data is proprietary.Model agnosticNot tied to one underlying AI model — able to route the same task across many models depending on cost, speed, or capability.Model CardA standardized documentation artifact that accompanies a machine learning model, reporting performance metrics broken down by demographic group and intersectional group. Used to detect and disclose bias before deployment.Model driftWhen a model's accuracy slips over time because the real-world data it sees no longer looks like the data it was trained on.Model GatewayA routing layer that automatically sends tasks to the cheapest model capable of handling them, enabling cost control at scale without sacrificing output quality.