QuickSight using ML

Question

I would like to use the ML model I created in AWS in my QuickSight reports.

• Is there a way to consume the ML endpoint in order to run batch predictions in QuickSight?
• Can I define a 'calculated field' in order to do that?

Answer 1

At this time there is no direct integration with AWS SageMaker and QuickSight, however you can use utilize SageMaker's batch transform jobs to convert data outside of QuickSight and then import this information into QuickSight for visualization. The output format for SageMaker's batch transform jobs is S3, which is a supported input data source for QuickSight.

• https://aws.amazon.com/about-aws/whats-new/2018/07/amazon-sagemaker-supports-high-throughput-batch-transform-jobs-for-non-real-time-inferencing/

Depending on how fancy you want to be, you can also integrate calls to AWS services such as AWS Lambda or AWS SageMaker as a user-defined function (UDF) within your datastore. Here are a few resources that may help:

• https://docs.aws.amazon.com/redshift/latest/dg/user-defined-functions.html
• https://aws.amazon.com/blogs/big-data/from-sql-to-microservices-integrating-aws-lambda-with-relational-databases/

Calculated fields will probably not help you in this regard - calculated fields are restricted to a relatively small set of operations, and none of these operations support calls to external sources.

• https://docs.aws.amazon.com/quicksight/latest/user/calculated-field-reference.html