5 ms·

"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.

levesque 4y ago
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$
- monkeydust 4y ago
  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.
  - petra 4y ago
    Any affordable(for a small business) no-code/low-code ML for small business?
    - blackbear_ 4y ago
      This one perhaps [1]? I only tried it for ten minutes but it seemed nice. [1] https://github.com/biolab/orange3 https://github.com/biolab/orange3
      - moffkalast 4y ago
        Ah Orange, that takes me back to university days. What a pain that thing was to work with.
        levesque 4y ago
        No joke, it was the clunkiest library ever. I wonder if they ended up improving it.
    - spywaregorilla 4y ago
      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.
    - jhfdbkofdchk 4y ago
      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 https://en.wikipedia.org/wiki/KNIME
CarbonCycles 4y ago
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?

levesque 4y ago

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$

monkeydust 4y ago
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.
- petra 4y ago
  Any affordable(for a small business) no-code/low-code ML for small business?
  - blackbear_ 4y ago
    This one perhaps [1]? I only tried it for ten minutes but it seemed nice. [1] https://github.com/biolab/orange3 https://github.com/biolab/orange3
    - moffkalast 4y ago
      Ah Orange, that takes me back to university days. What a pain that thing was to work with.
      - levesque 4y ago
        No joke, it was the clunkiest library ever. I wonder if they ended up improving it.
  - spywaregorilla 4y ago
    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.
  - jhfdbkofdchk 4y ago
    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 https://en.wikipedia.org/wiki/KNIME

CarbonCycles 4y ago

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?