Model (AI/ML Model)

A model is what you get when a machine learning algorithm is trained on data. The algorithm is the recipe for learning; the model is the result, a set of learned patterns (stored as parameters and weights) that can take new input and produce an output. That output might be a category, a number, a cluster, an embedding or generated text. When people say they “used AI” for a task, they almost always mean they sent data to a model.

How it works

A model goes through two phases. In training, the algorithm adjusts the model’s parameters so its outputs match patterns in the training data, with separate test and validation data used to check it generalises. In inference, the finished model is applied to new data: a sentiment model scores a new review, a classifier labels a new page.

Models differ by task (classification, regression, clustering, generation), by learning paradigm (supervised or unsupervised) and by architecture, from a decision tree to a neural network or a large language model with billions of parameters.

How it’s used in SEO and marketing

MLforSEO frames the practical choice as where your model comes from:

• Pre-trained: trained by a large organisation (Google, Amazon, Meta, OpenAI) and released ready to use through an API or library. For most marketers this is the right starting point.
• Fine-tuned: a pre-trained model adapted with a small, domain-specific dataset. Tools such as Google Cloud AutoML can specialise a model with as few as 100 to 200 labelled examples.
• Self-trained: built from scratch on your own data. It needs a genuinely unique domain, a large labelled dataset and real expertise.

Typical marketing uses include intent and page-type classifiers, sentiment and entity models for reviews, embedding models for internal linking and clustering, and LLMs for content transformation.

Example

To label 20,000 product reviews by sentiment, a team can call Google’s pre-trained Natural Language API and have results the same day. If the reviews use industry slang the general model misreads, they label a few hundred examples and fine-tune. Training their own model from scratch would only make sense if neither option worked and they had the data and skills to do it.

Limitations and things to watch

• A model only knows the patterns in its training data; it can be biased, out of date or wrong on unfamiliar inputs.
• Many models give slightly different outputs on each run, especially generative and unsupervised ones, which matters if stakeholders need consistent results.
• Complex models are harder to explain. See explainable AI.
• Always test a model against a labelled sample of your own data before relying on it.

Learn it on MLforSEO

• The Characteristics of a Machine Learning Problem: A Task, Data and Solution Framework for SEOs
• How to Find Machine-Learning-Enabled Automation in Your SEO Workflow
• When to use Machine Learning in SEO: Decision Checklist
• Introduction to Machine Learning for SEO