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ok531441 t1_ir0ew1c wrote

> Low code ML tools offer a bigger population within an organization to take part in ML development

Nope.

> while allowing flexible customization for the technical user.

Not at all.

So overall I would say no.

Seriously, who are the people in your organisation who know enough to work in ML but not enough to write code? (that's who the first part is supposedly enabling). And what no code tools give you better customisation than a programming language? (that claim is basically marketing nonsense)

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bilby_- OP t1_ir0fsgp wrote

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Low code tools can enable people who only know sql (some kind of analyst), to spin up and run a ML pipeline.

and if you look at tools such as dataiku and datarobot, they also allow the use of python to build more customized pipelines.

For example an insurance company can leverage a low code tool plus an analyst to test models such as predicting the probability for every elderly customer to buy life insurance and or for every young customer to purchase car insurance. The two inputs to the tool would be a population and target sql query. reading and writing to your dwh.

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generally in the data science world you have personas who are more technical or less. more technical can leverage docker, kubeflow, cicd etc. others work with jupyter notebooks. and maybe in the future only sql.

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dataslacker t1_ir0qfv8 wrote

In my experience sklearn/pandas is about the right level of “low code” in ML. Anything easier and you’re putting too much under the rug. Plus it’s free.

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nogear t1_ir139x3 wrote

If you are creating ML models you should now the science - low code will not save you from biases and other pitfalls.

Imagin letting a database guy doing a ML model, then use the probability for business decisions - just to find out, that your model is crap and highly biased.

And if you know the science you usually should be able to code with Python / Pandas / Sklearn ...

I am not against good and mighty tools - but typing the code is the simplest part in ML...

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Stats_Fast t1_ir29j0m wrote

>For example an insurance company can leverage a low code tool plus an analyst to test models such as predicting the probability for every elderly customer to buy life insurance and or for every young customer to purchase car insurance.

Insurance companies don't run their business like the marketing examples for low code environments.

They employ lots of highly qualified people with a range of skills across math/statistics/economics/legal/programming areas. Writing basic python isn't the constraint.

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cantfindaname2take t1_ir2vwe7 wrote

IMHO low code is fine for not resource intensive tasks. Once an analysts (without too much programming skills) starts running into performance or memory problems then that person will have a hard time adjusting their pipeline because the low code tools usually offer little flexibility.

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