Supportsoft Glossary

Transfer Learning to Accelerate AI Model Development

A pre-trained model can be adapted to a related but new task by using transfer learning. Instead of starting from the beginning and building a model from scratch, an organisation can build upon what already exists (i.e., use knowledge already created) to minimise both time and cost.

Transfer learning has a wide variety of applications within business when implemented properly. The primary benefit is that an organisation will be able to implement AI solutions significantly faster when the amount of data available is limited. Image Recognition (e.g., Face Recognition), Natural Language Processing, and Predictive Analytics are just some examples of the many areas in which transfer learning can be applied.

When applied to IT and ITES services, transfer learning allows for more efficient development of models, which ultimately results in reduced development costs associated with AI implementation. Transfer learning allows for the fine-tuning of pre-trained models to suit an organisation's needs without the need for extensive amounts of retraining.

Transfer learning provides a method to increase accuracy by utilising patterns learned from a large volume of data (i.e., models trained using large volumes of data will have higher accuracy when utilised for similar tasks). For organisations that are looking to implement AI at scale, transfer learning is a very effective method of implementing AI rapidly and with less complexity than previously required.