# Custom models
<a name="canvas-custom-models"></a>

In Amazon SageMaker Canvas, you can train custom machine learning models tailored to your specific data and use case. 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.

Canvas supports training a range of model types. After training a custom model, you can evaluate the model's performance and accuracy. Once satisfied with a model, you can make predictions on new data, and you also have the option to share the custom model with data scientists for further analysis or to deploy it to a SageMaker AI hosted endpoint for real-time inference, all from within the Canvas application.

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\$1 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 [How custom models work](canvas-build-model.md). For more information about the data types and sources supported for custom models, see [Data import](canvas-importing-data.md).
+ [Import your data](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-importing-data.html) 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](canvas-import-dataset.md).

  To learn more about sample datasets provided by SageMaker AI with which you can experiment, see [Sample datasets in Canvas](canvas-sample-datasets.md).
+ [Build](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-build-model.html) 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 the [Data Wrangler feature](canvas-data-prep.md). In Data Wrangler, you can create a data flow and use various data preparation techniques, such as applying advanced transforms or joining datasets. For image prediction models, you can [Edit an image dataset](canvas-edit-image.md) 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](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-evaluate-model.html) and determine how well it might perform on real-world data.
+ [Make single or batch predictions](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-make-predictions.html) with your model.