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That's interesting, I'm currently working on an idea that assumes the opposite. Having built specialized models for years, the cost of having a data science tea

That's interesting, I'm currently working on an idea that assumes the opposite. Having built specialized models for years, the cost of having a data science team clean the data and build a model is pretty high, and it can take quite a while (especially if part of the project is setting up the data collection). For prototyping and for smaller use cases, it makes a lot of sense to use a much more general model. Obviously this doesn't apply to things like medicine, etc. But for much more general things like: check if someone is on the train-tracks, or number of people currently queuing in a certain area, or if there's a fight in a stadium; I think multi-modal models are going to take over. Not because they're efficient, or particularly fast; but because it'll be quick to implement, test, and iterate on. The cost of building a specialized model, and keeping it up to date, will far exceed the cost of an LVM in most niche use cases.

tedivm 3y ago
I think it depends on how much you expect your model to be used, and how quickly the model needs to react. The higher either of those becomes the more likely you'll want to specialize. If you expect your model to be used a lot, and you don't have a way to distribute that pain (for instance, having a mobile app run the model locally on people's phones instead of remotely on your data centers) then it ends up being a cost balancing method. A single DGX machine with 8 GPUs is going to cost you about the same as a single engineer would. If cutting the model size down means you can reduce your number of machines that makes increasing headcount easier. The nice thing about data cleaning is that it's also an investment- you can keep using most data for a long time afterwords, and if you're smart then you're building automated techniques for cleaning that can be applied to new data coming in.

tedivm 3y ago

I think it depends on how much you expect your model to be used, and how quickly the model needs to react. The higher either of those becomes the more likely you'll want to specialize. If you expect your model to be used a lot, and you don't have a way to distribute that pain (for instance, having a mobile app run the model locally on people's phones instead of remotely on your data centers) then it ends up being a cost balancing method. A single DGX machine with 8 GPUs is going to cost you about the same as a single engineer would. If cutting the model size down means you can reduce your number of machines that makes increasing headcount easier. The nice thing about data cleaning is that it's also an investment- you can keep using most data for a long time afterwords, and if you're smart then you're building automated techniques for cleaning that can be applied to new data coming in.