AI Glossary
Transfer Learning
Applying knowledge from a pre-trained model to a new, related task. Transfer learning makes AI practical for business — you don't need millions of examples to build effective models.
Understanding Transfer Learning
Transfer learning is the principle that makes modern AI economically viable for businesses. Instead of training a model from scratch (which costs millions), you start with a pre-trained foundation model and adapt it to your specific task with a relatively small amount of your own data.
This is why a law firm can build an effective contract review AI with a few hundred labeled examples instead of millions — the foundation model already understands language, legal concepts, and document structure. Transfer learning adds your firm's specific terminology and preferences.
Both fine-tuning and RAG are forms of transfer learning. Fine-tuning adjusts model weights using your data. RAG provides your data as context at query time. Both leverage the foundation model's existing knowledge.
Transfer Learning in Canada
Canadian businesses benefit from transfer learning because they can build effective bilingual AI systems by adapting multilingual foundation models rather than training separate English and French models.
Related Services
Frequently Asked Questions
Instead of training from scratch (millions of dollars, months of work), transfer learning lets you adapt an existing model in hours or days using hundreds of examples instead of millions.
Fine-tuning is the most common method of transfer learning. It takes a pre-trained model and further trains it on your specific data, transferring the model's general knowledge to your domain.
See Transfer Learning in Action
Book a free 30-minute strategy call. We'll show you how transfer learning can drive real results for your business.