L o g i c M o u n t

Salesforce Data Cloud and Vertex AI

In the Salesforce ecosystem, this has been the year of artificial intelligence. Even though 2023 is almost over, announcements continue to be made.

According to a recent announcement from Salesforce, Google Vertex AI models are now widely accessible in Einstein Studio. I’ll go into more detail about this and why it matters in this post.

What is Google Vertex AI?

Let’s start by explaining what Google Cloud Vertex AI is. In essence, it is an easy-to-use platform for creating, implementing, and scaling machine learning applications and models. By providing options for both pre-built and custom models, it streamlines the process. With MLOps (Machine Learning Operations) tools to automate and scale workflows, it’s also efficient.

The platform has a number of benefits. First off, because of its end-to-end lifecycle management, ML projects move more quickly. Additionally, Google’s sophisticated AI algorithms and AutoML increase model accuracy. Then, with managed infrastructure and strong security protocols in place, scalability is guaranteed.

The real-world uses of Vertex AI include fraud detection in financial services, customer segmentation for tailored experiences, and predictive maintenance for averting equipment failures. With its ability to suggest products based on user interests and previous purchases, it is also useful for retail. Finally, it facilitates the use of chatbots for task automation and improved customer service.

What is Einsteine Studio?

What is Einstein Studio, then? With the help of this new technology from Salesforce, you can combine AI models from different predictive or generative AI services with the data from your business. This option enables a combination of external services’ AI models’ capabilities and your organization’s ownership of its data privacy.

Using a bring-your-own model (BYOM) strategy, data science and engineering teams can easily develop, train, and implement custom AI models within Salesforce. By using a zero-ETL framework, this method allows you to seamlessly integrate advanced AI models with existing data across various sectors, eliminating the time-consuming manual tasks associated with AI integrations and training. This approach enables you to create and train custom AI models for improved predictions and auto-generated content.

What are the Benefits?

One advantage of using a Google Cloud Vertex AI model with Data Cloud in Model Builder is that it gives you access to near real-time, curated, and harmonised data in Vertex AI. It enables speedy model development, testing, and tuning on a unified platform linked to Data Cloud, and it does away with the need for laborious ETL jobs, cutting costs and errors. This integration allows business processes in Salesforce Data Cloud to be automated using Vertex AI predictions through Flow and Apex. It also supports real-time, streaming, and batch data ingestion for pertinent AI outputs.

What is the Big Deal?

We’ve discussed the features of Google Vertex AI and the advantages of integrating it with Data Cloud, but why is this significant?

In other words, it demonstrates that Salesforce is aware that not all of your activities fall under the purview of its product line. After all, people have been utilising products like Amazon SageMaker and Google Vertex AI for years.

This further demonstrates the extent to which Model Builder can be used to integrate all of these technologies. Additionally, it’s all done with clicks rather than code in classic Salesforce fashion, making it usable by a far larger audience.


With data from sources like Google Cloud Vertex AI or Amazon SageMaker, data teams can develop, train, and apply AI models using Einstein Studio, an intuitive AI tool. The ultimate goal is to be able to make decisions based on real-time access to all of your data. This dream is now more attainable than ever thanks to Data Cloud, Einstein Studio, and collaborations like Google Cloud Vertex AI and Amazon SageMaker.


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