Salesforce has announced the general availability of a new BYOM (bring your own model) solution called Einstein Studio, which helps companies 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.

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.

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 harmonizes 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.