Google Cloud Vertex Al & Google Cloud NetApp Volumes

Rajendraprasad Hosamani from Google Cloud Storage presented on the integration of Google Cloud Vertex AI with Google Cloud NetApp Volumes, emphasizing the importance of grounding AI agents in bespoke, enterprise-specific data. He highlighted that AI workloads are diverse and that agents can significantly enhance user experiences by providing interactive, personalized, and efficient data sharing. For agents to be effective, they must be grounded in the specific truths of an organization, which requires seamless data integration from various sources, whether on-premises or in the cloud. This integration also necessitates robust governance to ensure data is shared appropriately within the enterprise.

Vertex AI, Google’s flagship platform for AI app builders, offers a comprehensive suite of tools categorized into model garden, model builder, and agent builder. The model garden allows users to select from first-party, third-party, or open-source models, while the model builder focuses on creating custom models tailored to specific business needs. The agent builder facilitates the responsible and reliable creation of AI agents, incorporating capabilities like orchestration, grounding, and data extraction. This platform supports no-code, low-code, and full-code development experiences, making it accessible to a wide range of users within an organization.

The integration of NetApp Volumes with Vertex AI enables the use of NetApp’s proven OnTap storage stack as a data store within Vertex AI. This allows for the seamless incorporation of enterprise data into AI development workflows, facilitating the creation, testing, and fine-tuning of AI agents. Raj demonstrated how this integration can elevate user experiences through various implementations, such as chat agents, search agents, and recommendation agents, all of which can be developed with minimal coding. This integration empowers organizations to leverage their accumulated data to create rich, natural language-based interactions for their end users, thereby enhancing the overall value derived from their AI investments.