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 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

Numeric

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+ category prediction

Predicting patient outcomes after being discharged from the hospital

Categorical

Local upload, Amazon S3, SaaS connectors

Time series forecasting

Predicting your inventory for the next quarter

Timeseries

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:

  • Determine your use case and type of model that you want to build. For more information about the custom model types, see Build a custom model. For more information about the data types and sources supported for custom models, see Import data into Canvas.

  • Import your data into Canvas. You can build a custom model with any tabular or image dataset that meets the input requirements. For more information about the input requirements, see Create a dataset.

    To learn more about sample datasets provided by SageMaker with which you can experiment, see Use sample datasets.

  • Build your custom model. You can do a Quick build to get your model and start making predictions more quickly, or you can do a Standard build for greater accuracy.

    For numeric, categorical, and time series forecasting model types, you can clean and prepare your data with features such as advanced transforms and joins. For image prediction models, you can Edit an image dataset to update your labels or add and delete images. Note that you can't use these features for multi-category text prediction models.

  • Evaluate your model's performance and determine how well it might perform on real-world data.

  • (Optional) For certain model types, you can collaborate with data scientists in Amazon SageMaker Studio Classic who can help review and improve your model.

  • Make single or batch predictions with your model.

Note

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.