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Seamlessly Integrate Machine Learning Predictions from Amazon SageMaker Canvas into Amazon QuickSight Dashboards

In today’s data-driven business landscape, the ability to make informed decisions based on accurate data is paramount. Business analysts play a pivotal role in this process by exploring, analyzing, interpreting, and identifying trends in data. Amazon QuickSight and Amazon SageMaker Canvas are powerful tools that empower business analysts to make sense of data and generate accurate ML predictions without requiring extensive ML expertise. This article will demonstrate how to seamlessly integrate ML predictions from Canvas into QuickSight dashboards, enabling accelerated decision-making and effective business outcomes.


– Ensure you’re using the same QuickSight Region as Canvas.
– Create an IAM policy for QuickSight access and attach it to your Studio execution role.
– Download the lending_club_loan_data_train.csv and lending_club_loan_data_test.csv datasets.

Solution Overview:

This solution involves configuring the right permissions to seamlessly redirect users from Canvas to QuickSight. It also covers building a model, running predictions, and demonstrating the business analyst experience.

Administrator Setup:

1. Create an IAM policy for QuickSight access.
2. Attach the policy to your Studio execution role.

Building a Model and Running Predictions:

1. Launch Canvas and import the training and validation datasets.
2. Create a new model and select the target column.
3. Choose Quick build to automatically generate a model.
4. Make predictions with the model using single prediction or batch prediction.

Sending Predictions to QuickSight:

1. Select the inferred batch dataset and choose Send to Amazon QuickSight.
2. Enter QuickSight user names to share the dataset and press Enter.
3. Choose Send to share data.

Business Analysts Experience:

1. Create an analysis on the batch prediction dataset shared from Canvas.
2. Create visuals to explore trends, risks, and business opportunities.
3. Publish the dashboard and share it with business stakeholders.


This solution enables business analysts to leverage ML predictions from Canvas in QuickSight dashboards, enhancing data-driven decision-making and driving effective business outcomes. This capability is available in all Regions where Canvas is supported.

About the Authors:

Ajjay Govindaram: Senior Solutions Architect at AWS, providing technical direction and design assistance for AI/ML application deployments.
Varun Mehta: Solutions Architect at AWS, helping customers build enterprise-scale well-architected solutions on the AWS Cloud.
Shyam Srinivasan: Principal Product Manager on the AWS AI/ML team, leading product management for Amazon SageMaker Canvas.