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Azure AutoML Model Accuracy Metrics

Hello Everyone , I have deployed a ML Model over a REST Endpoint successfully and the model is trained using Azure AutoML . I am successfully able to send data to the model via the endpoint and get back the predicted output . Sending of data is done in JSON format through a python script and the received data is print as output by the python script .I am able to view trained model metrics like accuracy , error rates , root mean squared errors etc .. through the Azure ML Studio User Interface but is there any way of accessing these error or accuracy metrics through the endpoint using a python script ? I know that there is ML flow but in that case the whole training should be done using a python script and that is not suitable for my case and the other one is logging APIs which don't give me error or accuracy metrics but give others which are not needed for my usage. In a nutshell I want to access model accuracy metrics of the already trained and deployed model via an API or endpoint . Also is there any place in azure storage where these accuracy metrics are stored and if they can be accessed via an Endpoint ? Please help me on this issue . Thank you all :)

azure-machine-learningazure-metrics-advisor
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@RahulRS-4040 Nice to e-meet you and thanks for reaching out to us.

Just want to cllarify, are you looking for how to check the model metrics in Azure Machine Learning Studio(designer) or want to build your model in Studio and check the metrics in Python SDK? Looking forward to your response.

Regards,
Yutong

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Hi there , I have the model deployed on an endpoint and I am able to view accuracy metrics of the model like RMS , variance etc. in the Azure ML Studio . I wanted to access these accuracy metrics of the deployed model on my local machine by running a python script and maybe use that to then display in a webpage . I have noticed that there are access to metric like endpoint requests , latency etc. But I specifically want to access the model accuracy metric available in studio UI via an endpoint or any other means on my local machine .

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