Azure Machine Learning retraining and scoring with Data Factory

My previously created Azure Machine Learning retraining and scoring model created with Azure Logic App and PowerBI (here is more info stopped working last January. I didn’t have enough motivation until now to start digging to find out what was wrong. Reason revealed to be removed component from Azure Logic App – namely Azure ML component. It just doesn’t exists any more.

I started to investigate what can I do to replace that solution and found this article: Instructions were a little bit outdated and missing some links to Azure ML, which gaps I try to fill with this article.

This process can be separated into three parts:

  1. Machine Learning model retraining
  2. Deploying retrained model
  3. Using updated model to scoring
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Azure Automated Machine Learning

I learned just recently about new feature what Azure Machine Learning holds – Auto Machine Learning (AutoML). According to Microsoft’s documentation:

Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify. The service then iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to “fit” your data.

Sounds good! I don’t have such a vast data science experience, so this kind of automated experimentation is just for people like me. There is no need for time consuming experimentation, because I don’t even know most of the algorithms.

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Liigan vedonlyöntikertoimia Azure Machine Learning:in avulla

Nyt olen testaillut eri vedonlyöntimalleja marraskuusta lähtien ja uskallan julkaista jotain tuloksia. Itse asiassa pyöritin kolmea eri mallia – Dixon & Coyles -mallia (tämä on ilmeisen suosittu malli ainakin fudisvedonlyönnissä), joka pohjautui tehtyihin maaleihin, neuroverkkomallia, joka pohjatui laskennallisiin maaleihin (ottaa huomioon maalien, laukauksien ja aloituksien määrän) sekä neuroverkkomallia, joka perustui puhtaasti voitto/tasapeli/tappio tietoon eli edellisten pelien maalimäärää ei huomioitu. Lisäksi hetken aikaa pyöritin edellisten pelien tehtyihin maalimääriin pohjautuvaa neuroverkkomallia, mutta tuo laskennallisiin maaleihin pohjautuva malli vaikutti paremmalta.

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