Azure ML:- How to retrain the Azure ML model using data from third party system every time the Azure ML web service is invoked

Question

I have a requirement wherein I need to fetch historical data from a third party system which is exposed as a web service and train the model on that data. I am able to achieve the above requirement by using "Execute Python Script" node and invoking the web service using python.

The main problem arises when I need to fetch data from the third party system every time the Azure ML web service is invoked, since the data in the third party system keeps on changing hence my Azure ML model should be trained for new data always.

I have gone through the link (https://learn.microsoft.com/en-us/azure/machine-learning/machine-learning-retrain-a-classic-web-service) but I am not sure how we can do this for my requirement as for me the new historical data set should be obtained every time the Azure ML web service is invoked.

Please suggest.

Thanks.

Answer 1

I recommend that you:

• look into the new Azure Machine Learning Service. Azure ML Studio (classic) is quite limited in what you can do, and
• consider creating a historical training set stored in Azure blob storage for the purposes of training, so that you only need to fetch from the 3rd party system when you have a trained model and would like to score the new records. To do so, check out this high-level guidance on how to use Azure Data Factory to create datasets for Azure Machine Learning