Model
Quick Answer
A model is the trained output of a machine learning process — a collection of learned parameters that, combined with an algorithm, can turn new inputs into predictions or generated content without being explicitly programmed for each case.
In Depth
What Model really means
When people talk about 'the AI', they usually mean the model. A model encodes what the system has learned from its training data. Once trained, a model can be deployed as an API, embedded into software, or downloaded and run locally, depending on the use case and sensitivity of the data.
Models vary enormously in size and cost. A compact classification model might be a few megabytes; a frontier large language model may be hundreds of gigabytes and require specialist hardware to serve at scale.
Why It Matters
Business relevance for UK organisations
Choosing the right model size is a classic cost/quality tradeoff. Many UK SMEs over-invest in the largest available model when a smaller, fine-tuned one would deliver 95% of the value at 5% of the cost.
Real-world example
How this shows up in practice
A Sheffield SaaS company replaced a general-purpose LLM with a fine-tuned smaller model for support ticket classification, reducing inference cost by 83%.
Related Terms
Continue exploring
Inference
Inference is the phase in which a trained model is used to produce predictions or outputs on new data. While training happens once (or periodically), inference happens every time a user interacts with an AI system, making it the dominant cost in most production deployments.
TechnicalFine-tuning
Fine-tuning is the process of further training a pre-trained model on a smaller, task-specific dataset so that it specialises in a particular domain, tone or set of behaviours. It sits between using a base model as-is and training a new model from scratch.
TechnicalLarge Language Model (LLM)
A Large Language Model (LLM) is a type of neural network trained on vast quantities of text to understand and generate human language. LLMs power chatbots, copilots, content generators and many modern AI features across consumer and business software.
AdvancedMLOps
MLOps is the discipline of operationalising machine learning: the practices, tools and culture needed to deploy, monitor, retrain and govern models reliably in production. It extends DevOps thinking to the unique challenges of data and models.