Azure ML analytics source
Azure ML is a machine-learning platform for automating, assuring, and accelerating predictive analytics, helping data scientists and analysts to build and deploy accurate predictive models.
The following Azure ML services are supported:
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Automated ML
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Designer
To connect to Azure ML, you must have created, or have access to, a model and deployed it to an endpoint on the Azure ML platform. This endpoint must be publicly accessible by Qlik Cloud.
Limitations
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Azure ML uses limits and quotas:
Manage and increase quotas for resources with Azure Machine Learning
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Azure quotas of deployed models and requests will impact and limit performance in the Qlik Sense reload and chart responsiveness when calling Azure ML endpoints.
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The Azure ML connector is limited to 200k rows per request. These are sent to the endpoint service in batches of 2k rows. In scenarios where more rows are required to be processed, use a Loop within the Data load script to process more rows in batches
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In a scenario where an application is regularly reloaded, it is best practice to cache the predictions using a QVD file and only send the new rows to the prediction endpoint. This will improve the performance of the Qlik Sense application reload and reduce the load on the Azure ML endpoint.
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When using Azure ML in a chart expression it is important to provide the data types of the fields as the model needs to process these in the correct string/numeric format. A limitation of server side extensions in chart expressions is that the data types are not automatically detected as they are in the load script.
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If you are using a relative connection name, and if you decide to move your app from a shared space to another shared space, or if you move your app from a shared space to your private space, then it will take some time for the analytic connection to be updated to reflect the new space location.