Quick Dev Tip: When to Use Transfer Learning vs. Fine-Tuning

Ever wondered if you should use Transfer Learning or go all-in with Fine-Tuning when adapting a pretrained model? 🤔

If you’re working with something like BERT or LLaMA 2, choosing the right approach can save you serious time and compute costs.

Here’s the quick breakdown:

• Transfer Learning is great when you just need to tweak the model a bit freeze most layers, retrain the final ones, and you’re good to go. Fast and efficient.

• Fine-Tuning, on the other hand, updates the whole model. It’s heavier, but sometimes necessary when your use case is highly domain-specific (think legal, medical, or financial AI).

We made a short video explainer that walks through both options and when to use each. If you’re building anything from sentiment classifiers to custom chatbots, it might help clarify the path forward.

Let us know if you're exploring this in your own AI project! 👀