Machine Learning Street Talk · Thursday, October 1, 2026
Shawn Wen advises enterprises to prioritize 'harnessing' over direct model fine-tuning, as it's more accessible and less costly. While fine-tuning offers greater control, it requires specialized expertise. He suggests starting with harnessing and only resorting to altering model weights if absolutely necessary, emphasizing that the goal is to achieve desired behavior without necessarily owning the deep technical complexities of the model itself.
“So I think that will require some help in terms of, even if they wanted to fine-tune the model. Or usually what I would suggest for a lot of our customers is that start with the harnessing. And if you really cannot get around that, models or tune your own models but there should be a second result you know that should be like you're number one by the parity immediately”
“But I would say that for a lot of the enterprises, that's probably not their primary area of expertise. So I think that will require some help in terms of, even if they wanted to fine-tune the model.”
“Because there's definitely some Danger in terms of going that deep is that, yeah, you can control the weight, but maybe you don't know how to do it the best way.”