Transfer Learning
Transfer learning is an ML technique where a model pre-trained on large datasets is transferred to a new, more specific task. Instead of starting from zero, the model's existing knowledge serves as a starting point and is adapted with few domain-specific data — saving significant time, compute power, and data requirements.
Why does this matter?
Transfer learning democratizes AI: even without millions of training data points and GPU clusters, companies can deploy powerful AI models. A pre-trained model fine-tuned on your 1,000 industry-specific documents often delivers better results than a model trained from scratch on 100,000 generic data points.
How IJONIS uses this
Transfer learning is our standard approach: we start with foundation models (Llama, Mistral, BERT) and adapt them via fine-tuning to your use case. For text tasks, we use Hugging Face Transformers; for image processing, we leverage pre-trained vision models. This achieves production quality in weeks instead of months.