Leverage AI/ML in your Salesforce apps using the Amazon SageMaker and Salesforce Data Cloud Integration
Introduction:
In this post, we will discuss the integration of Salesforce Data Cloud and Amazon SageMaker to create a predictive model for product recommendations. This is the second post in a series, and in Part 1, we demonstrated how businesses can securely access their Salesforce data using SageMaker and build models to activate predictions in Salesforce. In this post, we will focus on using Einstein Studio for product recommendations and show how to create a predictive model in SageMaker using historical data from Salesforce Data Cloud. We will also guide you through the process of setting up the integration and using the new capabilities launched in SageMaker for Salesforce. The solution overview provides a step-by-step guide, and we recommend using the newly launched SageMaker provided project template for Salesforce Data Cloud integration to streamline the implementation process. With this integration, you can easily train and deploy recommendation models in SageMaker and leverage the power of AI to improve customer experiences.
Full Article: Leverage AI/ML in your Salesforce apps using the Amazon SageMaker and Salesforce Data Cloud Integration
How to Use Einstein Studio for Product Recommendations
In this article, we will discuss how to use Einstein Studio, in conjunction with Salesforce Data Cloud and Amazon SageMaker, to create a predictive model for product recommendations. This integration allows businesses to access their Salesforce data securely using SageMaker and leverage its tools to build, train, and deploy models to endpoints hosted on SageMaker.
Solution Overview
To begin, we will demonstrate how to create a predictive model in SageMaker using historical data such as customer demographics, marketing engagements, and purchase history from Salesforce Data Cloud. We will utilize a sample dataset for this purpose.
Required Attributes
To create the model, the following attributes are needed:
– Club Member: Indicates whether the customer is a club member
– Campaign: The campaign the customer is a part of
– State: The state or province the customer resides in
– Month: The month of purchase
– Case Count: The number of cases raised by the customer
– Case Type Return: Indicates whether the customer returned any product within the last year
– Case Type Shipment Damaged: Indicates whether the customer had any shipments damaged in the last year
– Engagement Score: The level of engagement the customer has (response to mailing campaigns, logins to the online store, etc.)
– Tenure: The tenure of the customer relationship with the company
– Clicks: The average number of clicks the customer has made within a week prior to purchase
– Pages Visited: The average number of pages the customer has visited within a week prior to purchase
– Product Purchased: The actual product purchased
– ID: The ID of the record
– DateTime: The timestamp of the dataset
Building and Deploying the Model
The product recommendation model is built and deployed on SageMaker using the data from the Salesforce Data Cloud. Here are the steps involved in this process:
1. Set up the Amazon SageMaker Studio domain and establish OAuth between Salesforce and AWS.
2. Utilize the Amazon SageMaker Data Wrangler connector for Salesforce Data Cloud to prepare the data in SageMaker without copying it.
3. Train the recommendation model in SageMaker Studio using the prepared data.
4. Package the SageMaker Data Wrangler container and the trained recommendation model container in an inference pipeline.
5. Pass the real-time inference call data to the SageMaker Data Wrangler container, where it is preprocessed and passed to the trained model for product recommendation.
Algorithm Selection
While our example uses a specific algorithm to train the model, you have the flexibility to choose any algorithm suitable for your use case.
Templates for Streamlined Integration
To streamline the implementation of these steps, SageMaker provides project templates for Salesforce Data Cloud integration. These templates include:
1. An example notebook showcasing data preparation, model building, training, and registration.
2. A project template that automates the creation of a SageMaker endpoint hosting the inference pipeline model.
Model Registration and API Gateway
Once a version of the model is approved in the SageMaker Model Registry, the endpoint is exposed as an API with Amazon API Gateway using a custom Salesforce JSON Web Token (JWT) authorizer. API Gateway enables Salesforce Data Cloud to make predictions against the SageMaker endpoint using a JWT token.
Solution Architecture
The solution architecture includes the following components:
1. SageMaker Studio domain: This is the environment where the predictive model is built and deployed.
2. Salesforce connected app: This app enables the OAuth flow between SageMaker Studio and Salesforce Data Cloud.
3. AWS Secrets Manager: This is used to securely store the credentials from the Salesforce connected app.
4. IAM roles: These roles have the necessary permissions for accessing the secret in AWS Secrets Manager.
5. Model endpoint: This is created in SageMaker and registered in Salesforce Einstein Studio for making predictions.
Setting Up the Environment
To get started, follow these steps:
1. Create a SageMaker Studio domain.
2. Create a Salesforce connected app to enable OAuth integration.
3. Configure SageMaker permissions and lifecycle rules.
4. Create a secret in AWS Secrets Manager to store the credentials from the Salesforce connected app.
5. Configure a SageMaker lifecycle rule to grant access to the secret.
Conclusion
By leveraging the integration between Salesforce Data Cloud and Amazon SageMaker, businesses can create powerful predictive models for product recommendations. The step-by-step process outlined in this article provides a comprehensive guide for utilizing Einstein Studio and SageMaker to enhance customer experiences and drive sales.
Summary: Leverage AI/ML in your Salesforce apps using the Amazon SageMaker and Salesforce Data Cloud Integration
This post, co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI, discusses the integration of Salesforce Data Cloud and Amazon SageMaker. It explains how businesses can securely access their Salesforce data using SageMaker and use its tools to build, train, and deploy models for product recommendations. The post provides a solution overview and step-by-step instructions on how to create a predictive model in SageMaker using historical data from Salesforce Data Cloud. It also demonstrates how to set up the required domains, configure permissions, and use the new capabilities launched in SageMaker for Salesforce integration.
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