Use custom models - Amazon SageMaker

Use custom models

With Amazon SageMaker Canvas, you can build a custom model that is trained with your data. By training a custom model on your data, you are able to capture characteristics and trends that are specific and most representative of your data. For example, you might want to create a custom time series forecasting model that you train on inventory data from your warehouse that allows you to manage your logistics operations.

You can train a Canvas custom model on the following types of datasets:

  • Tabular (including Numeric, Categorical, Timeseries, and Text data)

  • Image

The following table shows the types of custom models that you can build in Canvas, along with their supported data types and data sources

Model type Example use case Supported data types Supported data sources

Numeric prediction

Predicting house prices based on features like square footage


Local upload, Amazon S3, SaaS connectors

2 category prediction

Predicting whether or not a customer is likely to churn

Binary or Categorical

Local upload, Amazon S3, SaaS connectors

3+ cateogry prediction

Predicting patient outcomes after being discharged from the hospital


Local upload, Amazon S3, SaaS connectors

Time series forecasting

Predicting your inventory for the next quarter


Local upload, Amazon S3, SaaS connectors

Single-label image prediction

Predicting types of manufacturing defects in images

Image (JPG, PNG)

Local upload, Amazon S3

Multi-category text prediction

Predicting categories of products, such as clothing, electronics, or household goods, based on product descriptions

Source column: Text

Target column: Binary or Categorical

Local upload, Amazon S3

Get started

To get started with building and generating predictions from a custom model, do the following:


If you already have a trained model in Amazon SageMaker Studio Classic that you’d like to share with Canvas, you can bring your own model to SageMaker Canvas. Review the BYOM prerequisites to determine whether your model is eligible for sharing.