{"id":7816,"date":"2022-09-20T05:04:00","date_gmt":"2022-09-20T12:04:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=7816"},"modified":"2022-09-20T05:04:00","modified_gmt":"2022-09-20T12:04:00","slug":"salesforce-aws-announcement-2022-dreamforce","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/au\/news\/stories\/salesforce-aws-announcement-2022-dreamforce\/","title":{"rendered":"Salesforce Deepens Partnership with AWS for New \u201cBring Your Own AI\u201d Innovations to Help Companies Bring Custom ML Models in Amazon SageMaker to the Salesforce Platform"},"content":{"rendered":"\n

Additional partnership momentum includes Amazon Ads integration with Salesforce Genie and the availability of new business applications and platform integrations. These innovations accelerate digital transformation and increase efficiency and productivity for sales, service, commerce, marketing, and IT teams.<\/em><\/p>\n\n\n\n

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Editor\u2019s Note: Salesforce Genie is now Salesforce Data Cloud. For the latest on Data Cloud,\u00a0go here<\/a>.<\/em><\/p>\n\n\n\n

Today at Dreamforce, Salesforce and AWS announced new integrations between the Salesforce Platform<\/a> and Amazon SageMaker<\/a>. The integrations will enable customers to use Amazon SageMaker, AWS\u2019s machine learning (ML) modeling service, alongside Einstein, Salesforce\u2019s artificial intelligence (AI) technology, to build new AI models tailored to the unique needs of their business \u2014\u00a0and use them in real time across the Customer 360.<\/p>\n\n\n\n

Now, data scientists and developers will have seamless access to real-time, unified, and cleansed customer data \u2013 alongside other data from their AWS data lake or data warehouse \u2013 for building and training ML models in Amazon SageMaker using their framework or tools of choice. These custom-built models can be used across the Salesforce Platform to power predictions and insights for customers. This drastically shortens time-to-value for custom AI investments by simplifying the process of training and deploying ML models for production use.<\/p>\n\n\n\n

Salesforce Genie<\/a> enables these integrations. Announced today, Genie is a real-time customer data platform that powers the Salesforce Customer 360. This new real-time customer data platform unlocks the ability to grant Amazon SageMaker with secure, native data access for model training, and also enables real-time inference calls to Amazon SageMaker to power AI predictions anywhere across the Salesforce Platform. <\/p>\n\n\n\n

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Customer experience is most impactful when it happens in the moment that matters to the customer, when it’s personalized, and when it’s comprehensive.<\/p>\nLiz Miller, Constellation Research<\/strong><\/cite><\/blockquote>\n\n\n\n

\u201cCustomer experience is most impactful when it happens in the moment that matters to the customer, when it’s personalized, and when it’s comprehensive. That requires a massive amount of real-time data, automation, and intelligence to train AI models to understand a brand\u2019s customer AND a brand\u2019s business. By empowering organizations to bring their own AI models, Salesforce and AWS are enabling teams to bring AI\u2026and the customer data now available across the enterprise with CDP\u2026out of the silos for the benefit of everyone,” said Liz Miller, Constellation Research.<\/strong><\/p>\n\n\n\n

The next chapter in Salesforce\u2019s AI strategy <\/h2>\n\n\n\n

These new integrations represent a shift to an open AI platform strategy for Salesforce, enabling customers with new options to meet the unique needs of their business. Customers can choose the purpose-built models or point-and-click model builders powered by Einstein, or leverage pre-trained or custom-built models trained and deployed with Amazon SageMaker, to power predictions across the Salesforce Customer 360.<\/p>\n\n\n\n

Amazon SageMaker complements existing Einstein AI capabilities by solving for the needs of data scientists and developers who want to use their preferred tools and ML frameworks within Amazon SageMaker to build, train, and deploy AI models that drive predictions across the Salesforce Platform.<\/p>\n\n\n\n\n\n

“Today we are building an automation program that creates custom propensity to buy scores for Salesforce accounts in real time. The Amazon SageMaker integration will allow us to automate and train the models behind the scores before importing them into Salesforce where our AEs can use them. That\u2019s just the beginning, though. We already see the possibilities to use Amazon SageMaker to train customized models for scoring leads, sales opportunities, and expansion efforts and easily make those actionable within Salesforce,\u201d said Dusty Vegas, Sr. Marketing Analytics Manager at Momentive.ai.<\/strong><\/p>\n\n\n\n

Einstein<\/a> generates over 175 billion predictions per day \u2013 powering predictions across all Salesforce products for sales, service, marketing, and commerce, and across every industry. Einstein includes an integrated set of AI technologies that makes the Customer 360 smarter. Amazon SageMaker is a fully managed end-to-end machine learning service that allows data scientists, developers, and business analysts to quickly and easily build and train machine learning models, and then directly deploy them into production.<\/p>\n\n\n\n

How the new integration works<\/h2>\n\n\n\n

Salesforce Genie ingests, harmonizes, and stores real-time data streams and transactional data at massive scale, and transforms it into a single, unified customer profile, or customer graph. This customer graph can now be accessed in real time from within Amazon SageMaker Data Wrangler alongside other data stored in AWS services, such as Amazon Simple Storage Service (Amazon S3) and Amazon Redshift. This drastically simplifies the process of accessing the right customer data for the ML model and provides customers with a low-code data preparation experience to accelerate their ML workflow. This makes it easier to train custom models for tasks like predicting a customer\u2019s propensity to churn or their potential lifetime value. <\/p>\n\n\n\n

Once custom AI models are trained and deployed on Amazon SageMaker, they can be registered for use across the Salesforce Platform, enabling business users, including sales, service, marketing, commerce, and information technology (IT) professionals to generate predictions or scores in real time, powered by Genie, to drive more personalized engagements and experiences for their customers. <\/p>\n\n\n\n

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Einstein makes it easy for our customers across every industry to get started with AI. With over 175 billion predictions per day across the Salesforce Customer 360, Einstein operates at a massive scale and helps our customers across every industry sell smarter, deepen customer relationships, scale customer support, and personalize experiences<\/p>\nRahul Auradkar, EVP and GM, Unified Data Services and Einstein at Salesforce<\/strong><\/cite><\/blockquote>\n\n\n\n

\u201cEinstein makes it easy for our customers across every industry to get started with AI. With over 175 billion predictions per day across the Salesforce Customer 360, Einstein operates at a massive scale and helps our customers across every industry sell smarter, deepen customer relationships, scale customer support, and personalize experiences,\u201d said Rahul Auradkar, EVP and GM, Unified Data Services and Einstein at Salesforce<\/strong>. \u201cBy opening our platform, we\u2019re enabling data scientists and developers to bring their own AI models with SageMaker, and quickly deploy custom AI into the Salesforce Platform. And, with Salesforce Genie, tap into real-time data that makes it easier than ever to hyper-personalize every moment and every application, in real time.\u201d<\/p>\n\n\n\n

\u201cThe biggest challenge customers face today isn\u2019t that they don\u2019t have data \u2014 it\u2019s that the data isn\u2019t connected, and it\u2019s difficult to glean business insights or easily put that data into action. With Amazon SageMaker and Salesforce, we aim to solve this challenge by enabling data scientists and developers to successfully build, deploy, and run high-quality machine learning models at scale,\u201d said Ankur Mehrotra, Director, Amazon SageMaker at AWS.<\/strong> \u201cAmazon SageMaker offers the deepest and broadest set of machine learning services and we\u2019re excited to help our joint customers accelerate innovation and time-to-value by bringing our collective product suites, ecosystems, and resources together.\u201d<\/p>\n\n\n\n

A partnership dedicated to customer growth<\/h2>\n\n\n\n

Salesforce and AWS have built and deployed out-of-the-box applications that bring voice, video, and productivity to sales, service, marketing, commerce, and IT teams \u2014 all infused with AI and real-time interactions. The partnership has also unified developer experiences, providing a suite of products and tools that enable developers to quickly build and deploy scalable business applications that accelerate their digital transformation. See what’s currently available from Salesforce and AWS here<\/a>.<\/p>\n\n\n\n