Submitted by kayhai t3_zf01qj in MachineLearning
I work in an oil refinery. Beyond my regular role, I have been working on Python-based analysis at my workplace, including machine learning. Many colleagues have sent their data to me for analysis or to create ML models, but I do not have time to process all the requests (though I’d love to).
I’m hoping to look for a no-code and low-cost method that empowers chemical/mechanical/electrical engineers (who have no Python or ML knowledge) to attempt ML studies on their data, before passing it to me for further work or to put into production.
We happen to be using Power BI for dashboarding. Is asking the engineers to use Power BI Premium Pay-per-user AutoML a good idea? Or are there better, or cheaper or easier to use platforms? Thanks for your advice.
Additional question: would anyone know the full list of models that are considered by Power BI’s automl? Googling doesn’t seem to give me such info.
rshah4 t1_izb32qr wrote
This is tough. I use to work for a large AutoML company that worked with oil and gas companies. It's difficult and often frustrating for non ML people to use AutoML tools. To use ML you need to know how to setup your problem - what is the target, partitioning data, . . It takes an understanding of ML to do this. Otherwise you will end up with people with 20 rows of data wanting to make a prediction or trying to use ML for something a simple rule would do or building a multilabel model where a binary model would have been better.
My suggestion is to keep them in the descriptive world, and if they want to move to ML, someone needs to introduce ML concepts to them before they start using the tools.