This has been the year of artificial intelligence in the Salesforce ecosystem. Although 2023 is coming to an end, the announcements just keep on coming.
Salesforce has recently announced that Google Vertex AI models are now generally available in Einstein Studio. In this post, I’ll delve into what this actually means and why it matters.
What is Google Vertex AI?
First things first, what is Google Cloud Vertex AI? Essentially, it is a user-friendly platform for building, deploying, and managing machine learning models and applications at scale. It simplifies the process, offering both pre-built and custom model options. Plus, it’s efficient, with MLOps (Machine Learning Operations) tools to automate and scale workflows.
The platform comes with several advantages. Firstly, it speeds up ML projects thanks to its end-to-end lifecycle management. Google’s advanced AI algorithms and AutoML also improve model accuracy. Scalability is then assured with managed infrastructure, and robust security standards are in place.
Vertex AI’s practical applications cover predictive maintenance for preventing equipment breakdowns, fraud detection in financial services, and customer segmentation for personalized experiences. It’s also handy for retail, offering product recommendations based on user interests and past purchases. Lastly, it supports chatbots for better customer support and task automation.
What is Einstein Studio?
Next up, what is Einstein Studio? This is a new technology from Salesforce that lets you merge your company data with AI models from various predictive or generative AI services. This option allows for a mix of data privacy (your organization retains ownership of its data) and the capabilities of AI models from external services.
Within Salesforce, data science and engineering teams can effortlessly create, train, and deploy custom AI models through a bring-your-own model (BYOM) approach. This approach allows you to create and train custom AI models for improved predictions and auto-generated content, eliminating the time-consuming manual tasks related to AI integrations and training by employing a zero-ETL framework, seamlessly integrating advanced AI models with existing data across various sectors.
What are the Benefits?
Using a Google Cloud Vertex AI model with Data Cloud in Model Builder provides several benefits, including access to curated, harmonized, and near real-time data in Vertex AI. It eliminates the need for tedious ETL jobs, reducing costs and errors, and allows quick model development, testing, and tuning on a unified platform connected to Data Cloud. This integration supports real-time, streaming, and batch data ingestion for relevant AI outputs and enables automation of business processes in Salesforce Data Cloud using Vertex AI predictions through Flow and Apex.
Check out this video to find out more.
Why is This a Big Deal?
We’ve covered what Google Vertex AI is and the benefits of using it with Data Cloud, but why is this a big deal?
To put it simply, it shows that Salesforce understands that not everything you do is within the Salesforce suite of products. People have been using tools like Google Vertex AI and Amazon SageMaker for years after all. This is something Lucy has spoken about in AI Wars: How Salesforce’s Agnostic LLM Approach Works.
What this also shows is just how powerful Model Builder is in giving you the capabilities to bring all of these technologies together. And in typical Salesforce fashion, it’s all done with clicks not code, making it accessible for a much wider audience to make use of.
Summary
Einstein Studio is a user-friendly AI tool that lets data teams create, train, and use AI models with data from places like Google Cloud Vertex AI or Amazon SageMaker. Ultimately, the dream is to have access to all your data in real time and be able to make decisions based on that data. With Data Cloud, Einstein Studio, and partnerships like Google Cloud Vertex AI and Amazon SageMaker, this dream is closer to a reality than ever before.