Salesforce has announced the general availability of Einstein Studio, a new, easy-to-use “bring your own model” (BYOM) solution that enables companies to use their custom AI models to power any sales, service, marketing, commerce, and IT application within Salesforce, helping them get more from their AI and data investments.
Einstein Studio makes it easy for data science and engineering teams to manage and deploy AI models more efficiently, and at lower cost. Companies can now easily use their proprietary company data from Salesforce Data Cloud to train models from Salesforce’s ecosystem of curated AI models, including Amazon SageMaker from Amazon Web Services (AWS), Google Cloud’s Vertex AI, and other AI services.
Einstein Studio trains AI models on proprietary customer data from Data Cloud, the first real-time data platform for CRM. Through this BYOM solution, customers will be able to use their custom AI models alongside turnkey LLMs provided through Einstein GPT, enabling them to deliver comprehensive AI fast.
Why it matters: Companies across every industry are rushing to integrate AI as IT leaders anticipate an enormous impact on their business. However, nearly 60% say they are still a year or two away from implementing AI solutions. And according to a Gartner® press release “On average, 54% of AI projects make it from pilot to production.”
The solution: Einstein Studio makes it faster and easier to run and deploy enterprise-ready AI across every part of the business, bringing trusted, open, and real-time AI experiences to every application and workflow.
How it works: With Einstein Studio, companies can leverage their proprietary, real-time customer data from Data Cloud to train AI models that solve specific business needs. And with Einstein Studio’s BYOM solution, companies can train their preferred AI model with Data Cloud, which connects all customer data from any source, and automatically harmonises that data into a single customer profile that adapts to each customer’s activity in real time for use across any department.
- Einstein Studio makes it faster to train AI models by providing pre-built, zero-ETL integration, which reduces the complexity of moving data between platforms. Einstein Studio allows technical teams to simply “point and click” to access their data in Data Cloud, then build and train their custom AI models for use across Salesforce applications. This process provides current and relevant customer data to inform AI predictions and auto-generate content.
- Einstein Studio provides a control panel for managing the use of AI models, empowering data scientists and engineers to govern how their data is exposed to AI platforms for training.
- Einstein Studio’s zero-ETL framework allows companies to power their custom AI models without the need for time-consuming data integration across systems. This means Data Cloud can connect to other AI tools without the extract, transform, and load (ETL) process, saving customers time and money while accelerating AI implementation.
What people are saying:
- “Companies need quick, ROI-driven AI investments that deliver value through actionable business insights and personalised customer experiences. Einstein Studio offers a faster, easier way to create and implement custom AI models, including a BYOM approach that allows customers to use the most relevant AI models – all while bypassing expensive ETL data pipeline processes. Now, Salesforce customers can harness their own proprietary data to power predictive and generative AI across every part of their organisation.” – Rahul Auradkar, EVP & GM, Unified Data Services & Einstein, Salesforce
- “Salesforce and Google Cloud share a commitment to helping businesses create real-world value with generative AI. Expanding access to Google’s powerful models for Salesforce customers through Einstein Studio means businesses can train AI models on Salesforce data, and then use the models throughout Salesforce’s business applications. In the future, this will allow customers to more easily deploy Google Cloud’s diverse models to address particular use cases, including Google’s foundation models, highly specialised models developed for specific industries, and a variety of open source models and those from our ecosystem of partners.” – Kevin Ichhpurani, Corporate VP, Global Ecosystem and Channels at Google Cloud
- “Tens of thousands of customers have tapped Amazon SageMaker to train models with billions of parameters and generate more than a trillion monthly predictions. That is why Amazon SageMaker has become the tool of choice for numerous customers since launching in 2017, but to make the most of their investment in machine learning, customers need access to data, which can be siloed and difficult to use. Working together with Salesforce, we are making it even easier for customers to bring together their Salesforce data with Amazon SageMaker, so they can take advantage of the breadth and depth of SageMaker features to fuel machine learning-powered insights and quickly take action on what they uncover.” – Swami Sivasubramanian, VP of Database, Analytics, and Machine Learning at AWS
- “Through Salesforce’s Data Cloud and Einstein Studio, customers will now have the ability to bring their own Amazon SageMaker models, providing them greater choice in how they utilise AI and customer data. The democratisation of such a capability is key to the success of our clients. Deloitte is excited to be a part of this journey and has developed a series of ‘bring your own models’ that our clients can leverage as part of the Salesforce ecosystem.” – David Geisinger, Global Alliance Lead, Deloitte Digital
