[HN Gopher] Show HN: Machine learning automation from creating t...
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Show HN: Machine learning automation from creating to using models
in production
Author : nidhaloff
Score : 54 points
Date : 2021-06-23 11:27 UTC (2 days ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| barbazoo wrote:
| Awesome tool!
|
| A few things I found so far:
|
| - When running "fit", I get an error when I use the "--yaml_file"
| parameter mentioned in the readme: "Unrecognized argument ->
| yaml_file". Using "-yml" works
|
| - When using the model for prediction, what format should
| "path_to_your_test_dataset.csv" be in? Is that a csv in the same
| format as the training set except for the column we want to
| predict? Edit: I just found
| https://github.com/nidhaloff/igel/blob/v0.4.0/examples/data/...
| nidhaloff wrote:
| Hi, thanks for your feedback! you are right, the
| argument/parameter is actually yaml_path and not yaml_file.
| This is a typo in the docs/readme, thanks for this finding ;)
| nidhaloff wrote:
| Hi all,
|
| Igel is a delightful tool to help you create, validate and use
| machine learning model (also in production) without writing code.
| You can use the integrated command line or the graphical
| interface.
|
| Igel uses FastAPI and uvicorn to serve your trained model, due to
| their high performance.
| mkl wrote:
| Hi, this looks interesting to me, but am I correct that it
| doesn't support image data?
|
| I spotted a typo in the README: "63 different machine learning
| model in igel" should say "models".
|
| The feature "Supports all state of the art machine learning
| models" seems absurd. How could it possibly be true? Surely
| there are many SOTA models not in SKLearn?
| kiranaxonator wrote:
| I would also like to recommend Axoantor Low Code Platform
| https://axonator.com/low-code-platform
| rush86999 wrote:
| this is not relevant
| nidhaloff wrote:
| Hi, thanks but how does this link to igel or to AI/ML
| elishay wrote:
| Cool project! Can probably integrate with SimpleML (author) to
| add persistence and versioning. Happy to help if you're
| interested https://github.com/eyadgaran/SimpleML
| th0ma5 wrote:
| I unfortunately would not recommend running this in production. I
| don't see anything like debugging or centralized or distributed
| logging either way, no performance metrics either from a raw code
| or stats performance measurements. I could go on, but there are
| many software projects out there that try to productionalize ML
| and know nothing about stats, but this is the opposite, it seems
| knowledgeable about statistical methods more than most of these
| kinds of things, but doesn't seem to have a production mindset.
| nidhaloff wrote:
| Thanks for the feedback! When I first started the project, it
| was not thought for production. Just for fast prototyping and
| experimenting with no efforts at all. However, users liked the
| tool and started requesting more features including support for
| serving models and eventually deploying (e.g this issue
| https://github.com/nidhaloff/igel/issues/62)
|
| I agree with your point of vue. However, igel is fairly new and
| evolving fast. Using igel to serve trained model is a new
| feature that was implemented in the new release so igel has a
| long way to go in order to be a solid product for production
| use.It will surely get more mature with time.
|
| Finally, notice that I didn't recommend running it in
| production. Just mentioned that it is possible and takes no
| efforts at all. However, if the user generated a trained model
| then anything can be done with it from there. Technically, the
| user can implement his/her own server and use the model as
| wanted. Obviously, users should do that if they want more
| control.
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(page generated 2021-06-25 23:01 UTC)