Amazon SageMaker Canvas
Amazon SageMaker Canvas gives you the ability to use machine learning to generate predictions without needing to write any code. The following are some use cases where you can use SageMaker Canvas:
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Predict customer churn
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Plan inventory efficiently
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Optimize price and revenue
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Improve on-time deliveries
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Classify text or images based on custom categories
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Identify objects and text in images
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Extract information from documents
With Canvas, you can chat with popular large language models (LLMs), access Ready-to-use models, or build a custom model trained on your data.
Canvas chat is a functionality that leverages open-source and Amazon LLMs to help you boost your productivity. You can prompt the models to get assistance with tasks such as generating content, summarizing or categorizing documents, and answering questions. To learn more, see Generative AI foundation models in SageMaker Canvas.
The Ready-to-use models in Canvas can extract insights from your data for a variety of use cases. You don’t have to build a model to use Ready-to-use models because they are powered by Amazon AI services, including Amazon Rekognition, Amazon Textract, and Amazon Comprehend. You only have to import your data and start using a solution to generate predictions.
If you want a model that is customized to your use case and trained with your data, you can build a model. You can get predictions customized to your data by doing the following:
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Import your data from one or more data sources.
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Build a predictive model.
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Evaluate the model's performance.
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Generate predictions with the model.
Canvas supports the following types of custom models:
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Numeric prediction (also known as regression)
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Categorical prediction for 2 and 3+ categories (also known as binary and multi-class classification)
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Time series forecasting
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Single-label image prediction (also known as image classification)
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Multi-category text prediction (also known as multi-class text classification)
You can also bring your own models into Canvas from Amazon SageMaker Studio Classic.
To learn more about pricing, see the SageMaker Canvas pricing page
SageMaker Canvas is currently available in the following Regions:
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US East (Ohio)
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US East (N. Virginia)
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US West (N. California)
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US West (Oregon)
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Asia Pacific (Mumbai)
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Asia Pacific (Seoul)
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Asia Pacific (Singapore)
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Asia Pacific (Sydney)
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Asia Pacific (Tokyo)
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Canada (Central)
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Europe (Frankfurt)
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Europe (Ireland)
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Europe (London)
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Europe (Paris)
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Europe (Stockholm)
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South America (São Paulo)
Topics
- Are you a first-time SageMaker Canvas user?
- Getting started with using Amazon SageMaker Canvas
- Tutorial: Build an end-to-end machine learning workflow in SageMaker Canvas
- Amazon SageMaker Canvas setup and permissions management (for IT administrators)
- Generative AI assistance for solving ML problems in Canvas using Amazon Q Developer
- Data import
- Data preparation
- Generative AI foundation models in SageMaker Canvas
- Ready-to-use models
- Custom models
- Logging out of Amazon SageMaker Canvas
- Limitations and troubleshooting
- Billing and cost in SageMaker Canvas
Are you a first-time SageMaker Canvas user?
If you are a first-time user of SageMaker Canvas, we recommend that you begin by reading the following sections:
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For IT administrators – Amazon SageMaker Canvas setup and permissions management (for IT administrators)
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For analysts and individual users – Getting started with using Amazon SageMaker Canvas
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For an example of an end to end workflow – Tutorial: Build an end-to-end machine learning workflow in SageMaker Canvas