[mpfundstein]

i am doing quite some deep learning work as part of my consultancy practice. And its all hand made stuff , together with lots of trial and error while trying to replicate papers that might be relevant fkr the task at hand.

so i totally agree with your statement. big corps overhype the shit out of it in order to sell and lots of n00bs fail for it.

regarding your last points. even if its super easy nowadays to deploy YOLO, and everyone and his mother can do it, making actually something that works and provides business value is hardcore. and without scientific skills_> no chance

[lordthirdeye]

if you don't mind me asking, what do you mean by "hand made". I'm currently wworking on a information science degree and I'm trying to focus on machine learning and data science. Could you go into a little more detail on what gives something business value?

[mpfundstein]

hand-make as in:

- looking very carefully into the very specific challenge of your client

- figuring out how (and if) ML can help

- figuring if its still economically feasible (costs of research vs perceived(!) benefit)

- deriving a solution.

- tinkering tinkering tinkering. usually more with the data than with the models :-)

All my A.I. projects are essentially outsourced R&D projects where we deliver the brain and computing power. So far, it never was as easy like installing YOLO or any other off the shelf product.

Edit: You also need very often custom software to create custom datasets. AI models are often only tested on academic datasets but I observed empirically that their performance transfers badly to real world datasets. So you need to create your own datasets etc. This is often a non-trivial problem. So I wrote a lot of dataset creation tools in my AI practice.

[disgruntledphd2]

Not the OP, but ultimately something that increases revenue or decreases costs by some measurable amount.

The best thing you can do to make yourself good at this is to practice. Get some Kaggle data and try to fit a model. Realise your data is crap, clean data, repeat.

Every useful system is hand made in the sense that there's a vast amount of set up and operational code. Mostly the model's the easy part (although it will take so much time to run).