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I've sold various "AI" consulting projects, I tell people that all the AI hard- tech is open source and that there's nothing that differentiates it. What is dif

I've sold various "AI" consulting projects, I tell people that all the AI hard- tech is open source and that there's nothing that differentiates it. What is different is implementation experience and industry customization. For example everyone has datasets scraped from the internet, but there are not deep application specific datasets publicly available. Likewise experience with the workflows in an industry. It's just software, there's little "secret sauce" in the engineering, it's the knowledge of the customer problem that's the differentiator.

PaulHoule 3y ago
I worked at a place where we thought there was value into putting it together in one neat package with a bow. That is, a lot of people are thinking at the level of "let's build a model" but for a business you will need to build a model and then update it repeatedly with new data as the world changes and your requirement changes. There would be a lot to say for a solution that includes tools for managing training sets, foundation models, training and evaluation, packages stuff up for inference in a repeatable way, etc. One trouble though is that you have to make about 20 decisions or so about how you do those things and developing that kind of framework people get some of them wrong and it will drive you crazy because other people will make different wrong decisions than you will. (To take an example, look at the model selection tools in scikit-learn and huggingface. Both of these are pretty good for certain things but they don't work together and both have serious flaws... And don't get me started with all the people who are hung up on F1 when they really should be using AUC...) So given the choice of (a) building out something half baked vs (b) fighting with various deficiencies in a packaged system, you can't blame people for picking (a) and "Just doing it". (Funny enough I always told people at that startup that we'd get bought by one of our customers, I thought it was going to be a big four accounting firm, a big telecom, or an international aerospace firm but... it turned out to be a famous shoe and clothing brand.)

PaulHoule 3y ago

I worked at a place where we thought there was value into putting it together in one neat package with a bow. That is, a lot of people are thinking at the level of "let's build a model" but for a business you will need to build a model and then update it repeatedly with new data as the world changes and your requirement changes. There would be a lot to say for a solution that includes tools for managing training sets, foundation models, training and evaluation, packages stuff up for inference in a repeatable way, etc. One trouble though is that you have to make about 20 decisions or so about how you do those things and developing that kind of framework people get some of them wrong and it will drive you crazy because other people will make different wrong decisions than you will. (To take an example, look at the model selection tools in scikit-learn and huggingface. Both of these are pretty good for certain things but they don't work together and both have serious flaws... And don't get me started with all the people who are hung up on F1 when they really should be using AUC...) So given the choice of (a) building out something half baked vs (b) fighting with various deficiencies in a packaged system, you can't blame people for picking (a) and "Just doing it". (Funny enough I always told people at that startup that we'd get bought by one of our customers, I thought it was going to be a big four accounting firm, a big telecom, or an international aerospace firm but... it turned out to be a famous shoe and clothing brand.)