Bring your own AI using Amazon SageMaker with Salesforce Data Cloud
This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI.
We’re excited to announce Amazon SageMaker and Salesforce Data Cloud integration. With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models. The inference endpoints are connected with Data Cloud to drive predictions in real time. As a result, businesses can accelerate time to market while maintaining data integrity and security, and reduce the operational burden of moving data from one location to another.
Introducing Einstein Studio on Data Cloud
Data Cloud is a data platform that provides businesses with real-time updates of their customer data from any touch point. With Einstein Studio, a gateway to AI tools on the data platform, admins and data scientists can effortlessly create models with a few clicks or using code. Einstein Studio’s bring your own model (BYOM) experience provides the capability to connect custom or generative AI models from external platforms such as SageMaker to Data Cloud. Custom models can be trained using data from Salesforce Data Cloud accessed through the Amazon SageMaker Data Wrangler connector. Businesses can act on their predictions by seamlessly integrating custom models into Salesforce workflows, leading to improved efficiency, decision-making, and personalized experiences.
Benefits of the SageMaker and Data Cloud Einstein Studio integration
Here’s how using SageMaker with Einstein Studio in Salesforce Data Cloud can help businesses:
- It provides the ability to connect custom and generative AI models to Einstein Studio for various use cases, such as lead conversion, case classification, and sentiment analysis.
- It eliminates tedious, costly, and error-prone ETL (extract, transform, and load) jobs. The zero-copy approach to data reduces the overhead to manage data copies, reduces storage costs, and improves efficiencies.
- It provides access to highly curated, harmonized, and real-time data across Customer 360. This leads to expert models that deliver more intelligent predictions and business insights.
- It simplifies the consumption of results from business processes and drives value without latency. For example, you can use automated workflows that can adapt in an instant based on new data.
- It facilitates the operationalization of SageMaker models and inferences in Salesforce.
The following is an example of how to operationalize a SageMaker model using Salesforce Flow.
SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
To streamline the SageMaker and Salesforce Data Cloud integration, we are introducing two new capabilities in SageMaker:
- The SageMaker Data Wrangler Salesforce Data Cloud connector – With the newly launched SageMaker Data Wrangler Salesforce Data Cloud connector, admins can preconfigure connections to Salesforce to enable data analysts and data scientists to quickly access Salesforce data in real time and create features for ML. This will enable users to access Salesforce Data Cloud securely using OAuth. You can interactively visualize, analyze, and transform data using the power of Spark without writing any code using the low-code visual data preparation features of Salesforce Data Wrangler. You can also scale to process large datasets with SageMaker Processing jobs, train ML modes automatically using Amazon SageMaker Autopilot, and integrate with a SageMaker inference pipeline to deploy the same data flow to production with the inference endpoint to process data in real time or in batch for inference.
- The SageMaker Projects template for Salesforce – We launched a SageMaker Projects template for Salesforce that you can use to deploy endpoints for traditional and large language models (LLMs) and expose SageMaker endpoints as an API automatically. SageMaker Projects provides a straightforward way to set up and standardize the development environment for data scientists and ML engineers to build and deploy ML models on SageMaker.
“The partnership between Salesforce and AWS Sagemaker will empower customers to leverage the power of AI (both, generative and non-generative models) across their Salesforce data sources, workflows and applications to deliver personalized experiences and power new content generation, summarization, and question-answer type experiences. By combining the best of both worlds, we are creating a new paradigm for data-driven innovation and customer success underpinned by AI.”
-Kaushal Kurapati, Salesforce Senior Vice President of Product, AI and Search
The BYOM integration solution provides customers with a native Salesforce Data Cloud connector in SageMaker Data Wrangler. The SageMaker Data Wrangler connector allows you to securely access Salesforce Data Cloud objects. Once users are authenticated, they can perform data exploration, preparation, and feature engineering tasks needed for model development and inference through the SageMaker Data Wrangler interactive visual interface. Data scientists can work within Amazon SageMaker Studio notebooks to develop custom models, which can be traditional or LLMs, and make them available for deployment by registering the model in the SageMaker Model Registry. When a model is approved for production in the registry, SageMaker Projects will automate the deployment of an invocation API that can be configured as a target in Salesforce Einstein Studio and integrated with Salesforce Customer 360 applications. The following diagram illustrates this architecture
In this post, we shared the SageMaker and Salesforce Einstein Studio BYOM integration, where you can use data in Salesforce Data Cloud to build and train traditional and LLMs in SageMaker. You can use SageMaker Data Wrangler to prepare data from Salesforce Data Cloud with zero copy. We also provided an automated solution to deploy the SageMaker endpoints as an API using a SageMaker Projects template for Salesforce.
AWS and Salesforce are excited to partner together to deliver this experience to our joint customers to help them drive business processes using the power of ML and artificial intelligence.
To learn more about the Salesforce BYOM integration, refer to Bring your own AI models with Einstein Studio. For a detailed implementation using product recommendations example use case, refer to Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce Apps with AI/ML.
About the Authors
Daryl Martis is the Director of Product for Einstein Studio at Salesforce Data Cloud. He has over 10 years of experience in planning, building, launching, and managing world-class solutions for enterprise customers including AI/ML and cloud solutions. He has previously worked in the financial services industry in New York City.
Rachna Chadha is a Principal Solutions Architect AI/ML in Strategic Accounts at AWS. Rachna is an optimist who believes that the ethical and responsible use of AI can improve society in the future and bring economic and social prosperity. In her spare time, Rachna likes spending time with her family, hiking, and listening to music.
Ife Stewart is a Principal Solutions Architect in the Strategic ISV segment at AWS. She has been engaged with Salesforce Data Cloud over the last 2 years to help build integrated customer experiences across Salesforce and AWS. Ife has over 10 years of experience in technology. She is an advocate for diversity and inclusion in the technology field.
Maninder (Mani) Kaur is the AI/ML Specialist lead for Strategic ISVs at AWS. With her customer-first approach, Mani helps strategic customers shape their AI/ML strategy, fuel innovation, and accelerate their AI/ML journey. Mani is a firm believer of ethical and responsible AI, and strives to ensure that her customers’ AI solutions align with these principles.