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The world of data is being re-engineered in real-time by AI. Today at Google Cloud Next '25 Tokyo, we introduced major innovations to Google's Data Cloud to help organizations harness this new reality. We're moving beyond human-led analysis to a collaborative partnership with intelligent agents. This begins with a new suite of specialized AI agents for every data user, from data engineers to business analysts. We're also providing developers with the foundational building blocks—including new Gemini Data Agents APIs—to build custom agents and create a truly interconnected agent ecosystem. All of this is powered by a unified, AI-native data foundation that erases the line between operational and analytical worlds, with innovations like the new Spanner columnar engine and the AI Query Engine in BigQuery. The agentic era is here, and we're incredibly excited to see what you'll build. More in blog by Yasmeen Ahmad https://lnkd.in/gaGrCpux

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At Microsoft’s AI for Good Lab, we’re working with partners at the University of Washington, the Fred Hutchinson Cancer Center, and other institutions to explore whether #AI can improve breast cancer screening. Read about our latest study published in Radiology: https://msft.it/6048sOWDn

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Our commitment at Google Cloud is to bring our leading AI capabilities to customers where they are. I'm thrilled to announce our expanded partnership with Oracle, which will make our #Gemini models available to customers via #VertexAI and the Oracle Cloud Infrastructure Generative AI service. Oracle is also collaborating with Google Cloud to make Gemini models available within Oracle Fusion Cloud Applications. This collaboration is a significant win for enterprises, enabling them to bring Google Cloud's powerful AI directly into their core data platforms and business workflows. https://lnkd.in/gPdtPXCH

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Just read an excellent deep-dive article written by our own AI Cloud Solution Architect Jian Sheng Ng on implementing Agentic AI frameworks using Azure Kubernetes Service (AKS) with a robust multi-tenancy approach. This is exactly the kind of architectural thinking we need as AI agents become more prevalent in enterprise environments. Key highlights that caught my attention: 🔒 Security-First Design: Complete tenant isolation at multiple layers - compute, data, and identity - eliminating cross-tenant data leakage risks 💰 Cost Optimization: Shared infrastructure with dedicated compute per tenant, allowing for optimal resource allocation and VM type selection based on tenant profiles 🏗️ Enterprise-Grade Architecture: • Control plane isolation through Kubernetes namespaces • Compute isolation via dedicated node pools with taints/tolerations • Identity isolation using Azure managed identities • Data isolation through RBAC and ABAC on storage accounts 🤖 Semantic Kernel Integration: Practical implementation showing how to deploy AI agents that can plan, reason, and execute function calls while maintaining strict security boundaries The solution elegantly addresses the “noisy neighbor” problem while providing the flexibility enterprises need for AI workloads at scale. The use of WorkloadIdentityCredential for secure, temporary credential delegation is particularly well-executed. For anyone working on enterprise AI deployments or multi-tenant architectures, this is a must-read case study with actual implementation details. Check out the full technical walkthrough: https://lnkd.in/gCmr6vYM #AzureKubernetes #AgenticAI #MultiTenancy #SemanticKernel #CloudArchitecture #AzureAI #Kubernetes #EnterpriseAI Tony Spelde Johnny Tian Jian Sheng Ng Sebastian Henze Hazirah Hasnan Pradeep Menon

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