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We have created an experiment in Azure ML Studio to predict some scheduling activities based on the system data and user data. System data consists of the CPU time, Heap Usage and other system parameters while user data has active sessions of the user and some user-specific data. Our experiment is working fine and returning the results quite similar to what we are expecting, but we are struggling with the following:-

1) Our experiment is not considering the updated data for training its models.

2) Every time we are required to upload the data and retrain the models manually.

I wonder if it is really possible to feed in live data to the azure experiments using some web-services or by using Azure DB. We are trying to update the data in CSV file that we have created in Azure storage. That probably would solve our 1st query.

Now, this updated data should be considered to train the model periodically automatically.

It would be great if someone could help us out with it?

Note: We are using our model using the web services created with the help of Azure studio.

2 Answers2

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You need to use Azure Data Factory to retrain the ML model.

You need to create a pipeline with the ML Batch Execution and ML Update Resource activities and to call your ML model you need to configure the endpoint on the webservice.

Here is some links to help you :

https://learn.microsoft.com/en-us/azure/data-factory/transform-data-using-machine-learning

https://learn.microsoft.com/en-us/azure/data-factory/update-machine-learning-models

Catapultaa
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    I have checked the above-mentioned links but it seems that the solution and steps mentioned are obsolete now. Ref: https://github.com/MicrosoftDocs/azure-docs/issues/6976. Can you please explain a more about the basic steps that we can follow. I shall try it with some custom activity. Thanks. –  Mar 06 '19 at 14:12
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Step 1 : Create 2 web services with Azure ML Studio ( One for the training model and one for the predictive model)

Step 2: Create endpoint through the web service with the link Manage Endpoint on Azure ML Studio for each web service

Step 3: Create 2 new connections on Azure Data Factory / Find Azure ML (on compute tab) and copy the Endpoint key and API Key that you will find under the Consume tab in the endpoint configuration (the one that you created on step 2) Endpoint Key = Batch Requests Key and API Key = Primary Key

Set Disable Update Resource for the training model endpoint Set Enable Update Resource for the predictive model endpoint ( Update Resource End Point = Patch key )

Step 4 : Create a pipeline with 2 activities ( ML Batch Execution and ML Update Resource) Set the AML Linked service for the ML batch Execution with the connection that has disable Update Resource

Set the AML Linked service for the ML Update Resource with the connection that has Enable Update Resource

Step 5 : Set the Web Service Inputs and Outputs

Catapultaa
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