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Comment by monkeydust | Hacker News Reader

"The compute time I spent for the whole project came to about $1,000"

Any comparative costs not using aws? Seems expensive for what he did.

From what I could see, the dataset has 300 data points (and less than 100 attributes/features), so it should take less than a minute to train a state of the art model on a normal computer. Cost: 0$

Right, it's a small dataset no GPU, guess your paying for aws software but I think there are plenty of open source equivalents that could match/beat these results with similar level of effort.

Any affordable(for a small business) no-code/low-code ML for small business?

This one perhaps [1]? I only tried it for ten minutes but it seemed nice.

[1] https://github.com/biolab/orange3

Ah Orange, that takes me back to university days. What a pain that thing was to work with.

No joke, it was the clunkiest library ever. I wonder if they ended up improving it.

Training an ML model is very easy. You can learn to pass things through an sklearn decision tree model over a couple hours.

Interpreting it is a more battle hardened task. Don't blindly trust such tools.

KNIME is a free, fairly well used tool in some sectors, for doing low and no-code analysis and machine learning models.

https://en.wikipedia.org/wiki/KNIME

I think the only reason why he used Sagemaker is that it provided him an easy out of the box set of tools that required minimum-to-no setup.

If the author took the time, he could have stitched together a completely zero-cost alternative that could have run locally on his box, but that would have gone against the argument that this is so easy that "even a monkey can do this". Don't get me wrong...AWS has some very nice libraries, especially their DL and encoding/decoding options.

I just think it was a bit heavy using Sagemaker especially for such a trivial use-case...maybe even the free Kaggle notebooks would have been a better alternative?

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