Maximizing Business Outcomes: Harness AWS Machine Learning with Amazon SageMaker Canvas

Maximizing Business Outcomes: Harness AWS Machine Learning with Amazon SageMaker Canvas

Maximizing Business Outcomes: Harness AWS Machine Learning with Amazon SageMaker Canvas

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Maximizing Business Outcomes: Harness AWS Machine Learning with Amazon SageMaker Canvas

In today’s data-driven world, Machine Learning (ML) plays an instrumental role in helping organizations generate revenue, reduce costs, improve efficiency, and drive quality. To facilitate this, Amazon SageMaker Canvas allows business analysts to generate accurate ML predictions using a point-and-click interface, without the need for coding or possessing any ML expertise. By integrating model development and sharing, businesses can foster tighter collaboration between their business and data science teams, further enhancing their outcomes.

Solution Overview:

To streamline the model creation and sharing process, there are three distinct architecture patterns that demonstrate how data scientists can share models with business analysts. These analysts can then directly generate predictions using Amazon SageMaker Canvas:

  1. Use Amazon SageMaker Autopilot and Canvas
  2. Use Amazon SageMaker JumpStart and Canvas
  3. Use SageMaker model registry and Canvas


Before training, building, and deploying a model using SageMaker and implementing it into Canvas, there are a few prerequisites:

  • Set up and onboard a SageMaker Studio user to a SageMaker domain.
  • Enable and set up Canvas base permissions for users and grant permissions to collaborate with Studio.
  • Have a trained model from Autopilot, JumpStart, or the model registry.

Use Autopilot and Canvas:

Amazon SageMaker Autopilot simplifies the AutoML process by automating critical tasks such as exploring data and selecting relevant algorithms for training. For instance, using a customer churn synthetic dataset, a data scientist can train, build, deploy, and share ML models with ease by leveraging both Autopilot and Canvas.

Use JumpStart and Canvas:

Amazon SageMaker JumpStart addresses common ML use cases by providing pre-built models and solutions. Data scientists can import a pre-built model from JumpStart into Canvas, significantly accelerating the prediction generation process.

Use SageMaker Model Registry and Canvas:

The SageMaker model registry plays a crucial role in managing, organizing, and deploying ML models. By registering a model with the SageMaker model registry, data scientists can provide business analysts with easy access to it, enabling them to generate predictions in Canvas effortlessly.

Integrating AWS machine learning models with Amazon SageMaker Canvas offers numerous benefits for optimizing business outcomes. Business analysts and data scientists can utilize these architecture patterns to not only enhance core business functions but also improve collaboration and drive effective business outcomes. By leveraging the power of AWS and SageMaker Canvas, companies will be better equipped to harness the full potential of machine learning to propel their operations forward.

Casey Jones Avatar
Casey Jones
1 year ago

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