This put up 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. Because of this, companies can speed up time to market whereas sustaining information integrity and safety, and cut back the operational burden of shifting information from one location to a different.
Introducing Einstein Studio on Knowledge Cloud
Knowledge Cloud is an information 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 number of clicks or utilizing code. Einstein Studio’s carry your individual mannequin (BYOM) expertise gives the aptitude to attach customized or generative AI fashions from exterior platforms corresponding to SageMaker to Knowledge Cloud. Customized fashions may be educated utilizing information from Salesforce Knowledge Cloud accessed via 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 personalised experiences.
Advantages of the SageMaker and Knowledge Cloud Einstein Studio integration
Right here’s how utilizing SageMaker with Einstein Studio in Salesforce Knowledge Cloud may also help companies:
- It gives the power to attach customized and generative AI fashions to Einstein Studio for varied use circumstances, corresponding to lead conversion, case classification, and sentiment evaluation.
- It eliminates tedious, pricey, and error-prone ETL (extract, remodel, and cargo) jobs. The zero-copy method to information reduces the overhead to handle information copies, reduces storage prices, and improves efficiencies.
- It gives entry to extremely curated, harmonized, and real-time information throughout Buyer 360. This results in professional 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 should use automated workflows that may adapt instantly primarily based on new information.
- It facilitates the operationalization of SageMaker fashions and inferences in Salesforce.
The next is an instance of the right way to operationalize a SageMaker mannequin utilizing Salesforce Flow.
SageMaker is a completely 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 shortly entry Salesforce information in actual time and create options for ML. It will allow customers to entry Salesforce Knowledge Cloud securely utilizing OAuth. You may interactively visualize, analyze, and remodel 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 routinely utilizing Amazon SageMaker Autopilot, and combine with a SageMaker inference pipeline to deploy the identical information circulate 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 should use to deploy endpoints for conventional and huge language fashions (LLMs) and expose SageMaker endpoints as an API routinely. SageMaker Tasks gives an easy method to arrange and standardize the event atmosphere for information scientists and ML engineers to construct and deploy ML fashions on SageMaker.
“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 personalised experiences and energy new content material era, summarization, and question-answer kind experiences. By combining the very 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
The BYOM integration resolution gives 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 growth and inference via the SageMaker Knowledge Wrangler interactive visible interface. Knowledge scientists can work inside Amazon SageMaker Studio notebooks to develop customized fashions, which may be conventional or LLMs, and make them obtainable for deployment by registering the mannequin within the SageMaker Mannequin Registry. When a mannequin is accepted 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 put up, we shared the SageMaker and Salesforce Einstein Studio BYOM integration, the place you should use information in Salesforce Knowledge Cloud to construct and practice conventional and LLMs in SageMaker. You should use SageMaker Knowledge Wrangler to arrange information from Salesforce Knowledge Cloud with zero copy. We additionally supplied an automatic resolution to deploy the SageMaker endpoints as an API utilizing a SageMaker Tasks template for Salesforce.
AWS and Salesforce are excited to accomplice collectively to ship this expertise to our joint prospects to assist them drive enterprise processes utilizing the facility of ML and synthetic intelligence.
To be taught extra concerning the Salesforce BYOM integration, check with Bring your own AI models with Einstein Studio. For an in depth implementation utilizing product suggestions instance use case, check with 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 trade 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 along with her household, 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 over the past 2 years to assist construct built-in buyer experiences throughout Salesforce and AWS. Ife has over 10 years of expertise in expertise. She is an advocate for range and inclusion within the expertise area.
Maninder (Mani) Kaur is the AI/ML Specialist lead for Strategic ISVs at AWS. Together 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 ideas.