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I have made an Azure Machine Learning Experiment which takes a small dataset (12x3 array) and some parameters and does some calculations using a few Python modules (a linear regression calculation and some more). This all works fine.

I have deployed the experiment and now want to throw data at it from the front-end of my application. The API-call goes in and comes back with correct results, but it takes up to 30 seconds to calculate a simple linear regression. Sometimes it is 20 seconds, sometimes only 1 second. I even got it down to 100 ms one time (which is what I'd like), but 90% of the time the request takes more than 20 seconds to complete, which is unacceptable.

I guess it has something to do with it still being an experiment, or it is still in a development slot, but I can't find the settings to get it to run on a faster machine.

Is there a way to speed up my execution?

1 Answer

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by (9.6k points)

You need to give some time for instances to execute otherwise your container will warm up inside and cause delays. 

Follow these steps to adjust the number of calls:

1. Go to

2. Navigate to Azure ML section on the left-side

3. Select your workspace

4. Go to web services 

5. Select your web service

6. Now, adjust the number of calls


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