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title: Use Amazon SageMaker Models
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locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_use_aws_sagemaker_models.md
canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_use_aws_sagemaker_models.htm&release=264.0.0&type=5
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description: You can build, train, and deploy custom machine learning (ML) models with Amazon SageMaker and data in Salesforce. After registering a model in SageMaker, define your prediction criteria and connect the model with Data 360 to get predictions and insights for your use case. Use the insights to enrich customer profiles, create segments, and personalize experiences across various channels.
products:
  - Data 360
release: "264"
---
# Use Amazon SageMaker Models

You can build, train, and deploy custom machine learning (ML) models with Amazon SageMaker and data in Salesforce. After registering a model in SageMaker, define your prediction criteria and connect the model with Data 360 to get predictions and insights for your use case. Use the insights to enrich customer profiles, create segments, and personalize experiences across various channels.

### Required Editions

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small edition"><colgroup><col></colgroup><tbody><tr class=""><td class="slds-cell-wrap" style="vertical-align:top;">Available in: Developer, Enterprise, Performance, and Unlimited editions.</td></tr></tbody></table>

Here are some benefits of using SageMaker models with Salesforce data.

- Access highly curated, harmonized, and near real-time data across Customer 360. This capability leads to expert models that deliver more intelligent predictions.
- Build models in SageMaker and easily connect them with Data 360 to consume predictive insights for business process optimization. Use cases include email engagement, propensity to buy, lead conversion, propensity to churn, and more.
- Operationalize models and inferences easily in Salesforce.Data 360 simplifies the consumption of results from business processes and drives value.
- Eliminate tedious, costly, and error-prone Extract, Transform, and Load (ETL) jobs from Amazon S3 using a zero-copy approach to data. This approach reduces costs and improves efficiencies for business.
- Integrate SageMaker models and get predictions in Salesforce. Use Flow, a workflow automation suite, to enable AI-driven decision-making that can evolve based on real-time updates to data.

To use a SageMaker model with Salesforce:

- Access your Data 360 data in SageMaker to build, train, and deploy a model.
- Connect your SageMaker model with Data 360 to consume the inferences for predictions and insights in various workflows across Salesforce.

![Data flow between a SageMaker Model and Data 360](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/data_cloud/images/c360_a_byom_aws_data_flow.png)

- **[Set Up Your Data in SageMaker](https://help.salesforce.com/s/articleView?id=data.c360_a_set_up_your_data.htm&release=264.0.0&type=5)**  
    Access Salesforce data from an Amazon SageMaker notebook using the Data 360 Python connector. The intuitive interface lets you quickly execute run queries and extract the data that you need.
- **[Connect a SageMaker Model](https://help.salesforce.com/s/articleView?id=data.c360_a_connect_your_external_model.htm&release=264.0.0&type=5)**  
    To consume your predictions in Salesforce, connect your SageMaker model with Data 360 and define the prediction criteria for your use case.

#### See Also

- [_External_: Bring your own AI using Amazon SageMaker with Salesforce Data Cloud](https://aws.amazon.com/blogs/machine-learning/bring-your-own-ai-using-amazon-sagemaker-with-salesforce-data-cloud/)