What Is a Pretrained AI Model?
Imagine trying to teach a toddler what a unicorn is. A good place to start might be by showing the child images of the creature and describing its unique features.
Now imagine trying to teach an artificially intelligent machine what a unicorn is. Where would one even begin?
A pretrained AI model is a deep learning model — an expression of a brain-like neural algorithm that finds patterns or makes predictions based on data — that’s trained on large datasets to accomplish a specific task. It can be used as is or further fine-tuned to fit an application’s specific needs.
Instead of building an AI model from scratch, developers can use pretrained models and customize them to meet their requirements.
To build an AI application, developers first need an AI model that can accomplish a particular task, whether that’s identifying a mythical horse, detecting a safety hazard for an autonomous vehicle or diagnosing a cancer based on medical imaging. That model needs a lot of representative data to learn from.
This learning process entails going through several layers of incoming data and emphasizing goals-relevant characteristics at each layer.
To create a model that can recognize a unicorn, for example, one might first feed it images of unicorns, horses, cats, tigers and other animals. This is the incoming data.
Then, layers of representative data traits are constructed, beginning with the simple — like lines and colors — and advancing to complex structural features. These characteristics are assigned varying degrees of relevance by calculating probabilities.
As opposed to a cat or tiger, for example, the more like a horse a creature appears, the greater the likelihood that it is a unicorn. Such probabilistic values are stored at each neural network layer in the AI model, and as layers are added, its understanding of the representation improves.
To create such a model from scratch, developers require enormous datasets, often with billions of rows of data. These can be pricey and challenging to obtain, but compromising on data can lead to poor performance of the model.
Precomputed probabilistic representations — known as weights — save time, money and effort. A pretrained model is already built and trained with these weights.
Using a high-quality pretrained model with a large number of accurate representative weights leads to higher chances of success for AI deployment. Weights can be modified, and more data can be added to the model to further customize or fine-tune it.
