Traditionally, getting ML predictions from trained models into a BI tool used by business teams requires substantial heavy lifting. You have to write code to ETL the data into Amazon Simple Storage Service (Amazon S3), call the inference API to get the predictions, ETL the model output from Amazon S3 to a queryable source, orchestrate this process whenever new data is available, and repeat the workflow for every new model. In this tech talk, learn how data scientists, ML, and data engineers can easily use Amazon QuickSight’s integration with Amazon SageMaker and automate the entire data movement and ML inference pipeline, enabling access of their ML models to new business users within their organizations.
Learning Objectives:
*Learn how SageMaker users can use QuickSight for ML predictions and grant access to business teams
*Learn how data scientists and data engineers can integrate ML into BI tools
*Learn how business analysts can do data science using QuickSight
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25 июл 2021
