Deliver your personal AI utilizing Amazon SageMaker with Salesforce Knowledge Cloud

This publish is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI.

We’re excited to announce Amazon SageMaker and Salesforce Knowledge Cloud integration. With this functionality, companies can entry their Salesforce information securely with a zero-copy method utilizing SageMaker and use SageMaker instruments to construct, practice, and deploy AI fashions. The inference endpoints are linked with Knowledge Cloud to drive predictions in actual time. In consequence, companies can speed up time to market whereas sustaining information integrity and safety, and scale back the operational burden of shifting information from one location to a different.

Introducing Einstein Studio on Knowledge Cloud

Knowledge Cloud is a knowledge platform that gives companies with real-time updates of their buyer information from any contact level. With Einstein Studio, a gateway to AI instruments on the info platform, admins and information scientists can effortlessly create fashions with a couple of clicks or utilizing code. Einstein Studio’s convey your personal mannequin (BYOM) expertise offers the potential to attach customized or generative AI fashions from exterior platforms resembling SageMaker to Knowledge Cloud. Customized fashions will be skilled utilizing information from Salesforce Knowledge Cloud accessed by the Amazon SageMaker Data Wrangler connector. Companies can act on their predictions by seamlessly integrating customized fashions into Salesforce workflows, resulting in improved effectivity, decision-making, and customized experiences.

Advantages of the SageMaker and Knowledge Cloud Einstein Studio integration

Right here’s how utilizing SageMaker with Einstein Studio in Salesforce Knowledge Cloud will help companies:

  • It offers the power to attach customized and generative AI fashions to Einstein Studio for numerous use instances, resembling lead conversion, case classification, and sentiment evaluation.
  • It eliminates tedious, pricey, and error-prone ETL (extract, rework, and cargo) jobs. The zero-copy method to information reduces the overhead to handle information copies, reduces storage prices, and improves efficiencies.
  • It offers entry to extremely curated, harmonized, and real-time information throughout Buyer 360. This results in knowledgeable fashions that ship extra clever predictions and enterprise insights.
  • It simplifies the consumption of outcomes from enterprise processes and drives worth with out latency. For instance, you need to use automated workflows that may adapt instantly based mostly on new information.
  • It facilitates the operationalization of SageMaker fashions and inferences in Salesforce.

The next is an instance of tips on how to operationalize a SageMaker mannequin utilizing Salesforce Flow.

SageMaker integration

SageMaker is a totally managed service to arrange information and construct, practice, and deploy machine studying (ML) fashions for any use case with totally managed infrastructure, instruments, and workflows.

To streamline the SageMaker and Salesforce Knowledge Cloud integration, we’re introducing two new capabilities in SageMaker:

  • The SageMaker Knowledge Wrangler Salesforce Knowledge Cloud connector – With the newly launched SageMaker Knowledge Wrangler Salesforce Knowledge Cloud connector, admins can preconfigure connections to Salesforce to allow information analysts and information scientists to rapidly entry Salesforce information in actual time and create options for ML. This can allow customers to entry Salesforce Knowledge Cloud securely utilizing OAuth. You may interactively visualize, analyze, and rework information utilizing the facility of Spark with out writing any code utilizing the low-code visible information preparation options of Salesforce Knowledge Wrangler. You too can scale to course of giant datasets with SageMaker Processing jobs, practice ML modes mechanically utilizing Amazon SageMaker Autopilot, and combine with a SageMaker inference pipeline to deploy the identical information stream to manufacturing with the inference endpoint to course of information in actual time or in batch for inference.

  • The SageMaker Tasks template for Salesforce – We launched a SageMaker Projects template for Salesforce that you need to use to deploy endpoints for conventional and huge language fashions (LLMs) and expose SageMaker endpoints as an API mechanically. SageMaker Tasks offers an easy option to arrange and standardize the event surroundings for information scientists and ML engineers to construct and deploy ML fashions on SageMaker.

Associate Quote

“The partnership between Salesforce and AWS Sagemaker will empower prospects to leverage the facility of AI (each, generative and non-generative fashions) throughout their Salesforce information sources, workflows and functions to ship customized experiences and energy new content material technology, summarization, and question-answer sort experiences. By combining one of the best of each worlds, we’re creating a brand new paradigm for data-driven innovation and buyer success underpinned by AI.”

-Kaushal Kurapati, Salesforce Senior Vice President of Product, AI and Search

Answer overview

The BYOM integration answer offers prospects with a local Salesforce Knowledge Cloud connector in SageMaker Knowledge Wrangler. The SageMaker Knowledge Wrangler connector permits you to securely entry Salesforce Knowledge Cloud objects. As soon as customers are authenticated, they’ll carry out information exploration, preparation, and have engineering duties wanted for mannequin improvement and inference by the SageMaker Knowledge Wrangler interactive visible interface. Knowledge scientists can work inside Amazon SageMaker Studio notebooks to develop customized fashions, which will be conventional or LLMs, and make them obtainable for deployment by registering the mannequin within the SageMaker Mannequin Registry. When a mannequin is accredited for manufacturing within the registry, SageMaker Tasks will automate the deployment of an invocation API that may be configured as a goal in Salesforce Einstein Studio and built-in with Salesforce Buyer 360 functions. The next diagram illustrates this structure


On this publish, we shared the SageMaker and Salesforce Einstein Studio BYOM integration, the place you need to use information in Salesforce Knowledge Cloud to construct and practice conventional and LLMs in SageMaker. You need to use SageMaker Knowledge Wrangler to arrange information from Salesforce Knowledge Cloud with zero copy. We additionally supplied an automatic answer to deploy the SageMaker endpoints as an API utilizing a SageMaker Tasks template for Salesforce.

AWS and Salesforce are excited to associate collectively to ship this expertise to our joint prospects to assist them drive enterprise processes utilizing the facility of ML and synthetic intelligence.

To study extra in regards to the Salesforce BYOM integration, seek advice from Bring your own AI models with Einstein Studio. For an in depth implementation utilizing product suggestions instance use case, seek advice from Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce Apps with AI/ML.

Concerning the Authors

Daryl Martis is the Director of Product for Einstein Studio at Salesforce Knowledge Cloud. He has over 10 years of expertise in planning, constructing, launching, and managing world-class options for enterprise prospects together with AI/ML and cloud options. He has beforehand labored within the monetary companies business in New York Metropolis.

Rachna Chadha is a Principal Options Architect AI/ML in Strategic Accounts at AWS. Rachna is an optimist who believes that the moral and accountable use of AI can enhance society sooner or later and produce financial and social prosperity. In her spare time, Rachna likes spending time together with her household, mountain climbing, and listening to music.

Ife Stewart is a Principal Options Architect within the Strategic ISV section at AWS. She has been engaged with Salesforce Knowledge Cloud during the last 2 years to assist construct built-in buyer experiences throughout Salesforce and AWS. Ife has over 10 years of expertise in know-how. She is an advocate for variety and inclusion within the know-how discipline.

Maninder (Mani) Kaur is the AI/ML Specialist lead for Strategic ISVs at AWS. Along with her customer-first method, Mani helps strategic prospects form their AI/ML technique, gasoline innovation, and speed up their AI/ML journey. Mani is a agency believer of moral and accountable AI, and strives to make sure that her prospects’ AI options align with these rules.

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