Using open source models might be good for quickly experimenting and getting a feel/sense of the value of an approach for a particular problem. But at a company (especially big tech companies), there are many more things to consider:
How do I scale this to my particular dataset? It’s a bigger pain to change my data to fit a given model than to change the model to fit my data
How can I integrate my company’s infrastructure/tooling/monitoring to this? Often it ends up being simpler to revisit the implementation from scratch
How easy is it to experiment with adjustments to this? Often we don’t want to pick a single architecture forever, so we want to be able to adjust and modify easily. Open source models may not always accommodate this.
At the risk of being flippant/dismissive: coding up a model/architecture is one of the easiest and fastest parts of the problem. So if you can make other things easier by making a model implementation from scratch, it’s makes sense to just do that.
TheTwigMaster t1_j5097m6 wrote
Reply to [D] ML Researchers/Engineers in Industry: Why don't companies use open source models more often? by tennismlandguitar
Using open source models might be good for quickly experimenting and getting a feel/sense of the value of an approach for a particular problem. But at a company (especially big tech companies), there are many more things to consider:
At the risk of being flippant/dismissive: coding up a model/architecture is one of the easiest and fastest parts of the problem. So if you can make other things easier by making a model implementation from scratch, it’s makes sense to just do that.