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:
- Use Amazon SageMaker Autopilot and Canvas
- Use Amazon SageMaker JumpStart and Canvas
- Use SageMaker model registry and Canvas
Prerequisites:
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
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Disclaimer
*The information this blog provides is for general informational purposes only and is not intended as financial or professional advice. The information may not reflect current developments and may be changed or updated without notice. Any opinions expressed on this blog are the author’s own and do not necessarily reflect the views of the author’s employer or any other organization. You should not act or rely on any information contained in this blog without first seeking the advice of a professional. No representation or warranty, express or implied, is made as to the accuracy or completeness of the information contained in this blog. The author and affiliated parties assume no liability for any errors or omissions.