Skip to main content Skip to complementary content

Amazon SageMaker analytics source

Amazon SageMaker is a machine-learning platform for automating, assuring, and accelerating predictive analytics, helping data scientists and analysts to build and deploy accurate predictive models.

To connect to Amazon SageMaker, you must have created, or have access to, a model and deployed it to an endpoint on the AWS platform. This endpoint must be publicly accessible by Qlik Cloud.

Amazon SageMaker


  • Amazon Comprehend has endpoints quotas:

    Amazon SageMaker endpoints and quotas

  • AWS offers to deploy model on instance types, for example medium and large instance types. The resources available on the Amazon services will impact and limit performance in the Qlik Sense reload and chart responsiveness.

  • When Qlik Sense sends data to Amazon SageMaker it is sent in a CSV format without a header row. This means the fields need to be sent in the exact order which the Amazon SageMaker endpoint is expecting them in. You must specify the fields in the same order as they were when the model was generated.

  • The Amazon SageMaker connector is limited to 200,000 rows per request. These are sent to the endpoint service in batches of 2000 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.

  • 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 Amazon SageMaker endpoint.

  • When using Amazon SageMaker 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.

  • 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.

Did this page help you?

If you find any issues with this page or its content – a typo, a missing step, or a technical error – let us know how we can improve!