All news with #google cloud tag

💬 Google Cloud has made Looker Conversational Analytics generally available, bringing natural-language data queries to all Looker users. Built on the Looker semantic layer and powered by Gemini and Google’s agentic frameworks, the feature provides instant, explainable answers and supports multi-turn exploration across up to five connected Explores. Analysts can build and share agents, use LookML for fine tuning, and rely on a governed foundation that surfaces “How was this calculated?” explanations. Admins can enable the capability now to accelerate data discovery and improve self-service across teams.

🔒 Fortinet has been named the inaugural Google Unified Security Recommended partner for network protection, integrating FortiSASE and the FortiGate NGFW to run natively on Google Cloud. The integration delivers unified policy and shared telemetry with Google Security Operations, combining FortiGuard Labs and Google threat intelligence for AI-driven detection and response. Customers gain consolidated management, improved performance via Google’s backbone, and simplified procurement through the Google Cloud Marketplace.

🤖 This post outlines a concise four-step framework for startups to design and deploy multi-agent systems, illustrated through a Sales Intelligence Agent example. It recommends choosing between pre-built, partner, or custom agents and describes using Google's Agent Development Kit (ADK) for code-first control. The guide covers hybrid architectures, tool-based state isolation, secure data access, and a three-step deployment blueprint to run agents on Vertex AI Agent Engine and Cloud Run.

🔒 Google Cloud is launching the Google Unified Security Recommended program to validate deep integrations between its security portfolio and third-party vendors. Inaugural partners CrowdStrike, Fortinet, and Wiz bring endpoint, network, and multicloud CNAPP capabilities into Google Security Operations. Partners commit to cross-product technical integration, a collaborative support model, and investment in AI initiatives such as the model context protocol (MCP). Qualified solutions will be available via Google Cloud Marketplace for simplified procurement and consolidated billing.

🤝 Google Cloud and Hugging Face are deepening their partnership to speed developer workflows and strengthen enterprise model deployments. A new gateway will cache Hugging Face models and datasets on Google Cloud so downloads take minutes, not hours, across Vertex AI and Google Kubernetes Engine. The collaboration adds native TPU support for open models and integrates Google Cloud’s threat intelligence and Mandiant scanning for models served through Vertex AI.

🤝 Google Cloud is increasing local AI compute in India with its AI Hypercomputer powered by Trillium TPUs, enabling training and serving of advanced Gemini models with data residency and sovereignty controls. New local offerings include batch support for Gemini 2.5 Flash, a preview of Document AI, and real‑time grounding using Google Maps for location‑aware responses. Google is also supporting Indic Arena at IIT Madras with cloud credits to benchmark Indian multilingual models and to help grow the local AI ecosystem.

🚀 Zeotap migrated its Customer Data Platform from ScyllaDB to Bigtable to address scaling challenges, operational overhead, and highly spiky workloads. The cloud-native stack—using Dataflow, a home-grown streaming engine, Memorystore as a cache, Bigtable as the hot store, and BigQuery for analytics—delivers predictable low-latency reads and writes at scale. The transition yielded a 46% reduction in TCO and a ~20% drop in operational tasks while enabling sub-second SLAs and faster ML deployment.

🚀 Google Cloud announces general availability of the N4D machine series built on 5th Gen AMD EPYC 'Turin' processors and Google's Titanium infrastructure. N4D targets cost-optimized, general-purpose workloads — web and app servers, data analytics, and containerized microservices — with up to 96 vCPUs, 768 GB DDR5, 50 Gbps networking, and Hyperdisk storage. Google cites up to 3.5x web-serving throughput versus N2D and material price-performance gains for general compute and Java workloads.

🚀 Google Cloud describes a full-stack solution for running high-scale Reinforcement Learning (RL) with LLMs, combining custom TPU hardware, NVIDIA GPUs, and optimized software libraries. The approach addresses RL's hybrid demands—reducing sampler latency, easing memory contention across actor/critic/reward models, and accelerating weight copying—by co-designing hardware, storage (Managed Lustre, Cloud Storage), and orchestration on GKE. The blog emphasizes open-source contributions (vLLM, llm-d, MaxText, Tunix) and integrations with Ray and NeMo RL recipes to improve portability and developer productivity. It also highlights mega-scale orchestration and multi-cluster strategies to run production RL jobs at tens of thousands of nodes.

🔒 Google Public Sector announced it has achieved CMMC Level 2 certification, validated by a certified third-party assessment organization (C3PAO). The certification confirms that its internal systems used to process and store Controlled Unclassified Information (CUI) meet DoD cybersecurity expectations. While the certification covers Google’s internal systems and does not extend to customer environments, Google highlights support for the Defense Industrial Base through FedRAMP-authorized cloud services and published compliance resources, including a Google Workspace CMMC Implementation Guide, to help partners accelerate their own CMMC journeys.

🔒 Ericsson has implemented a global data governance framework using Dataplex Universal Catalog on Google Cloud to ensure data integrity, discoverability, and compliance across its Managed Services operation. The program standardized a business glossary, automated quality checks with incident-driven alerts, and visualized column-level lineage to support analytics, AI, and automation at scale. It balances defensive compliance with offensive innovation and embeds stewardship through Ericsson’s Data Operating Model.

🚀 Deploy the official n8n Docker image to Cloud Run in minutes to run scalable, serverless AI workflows. Cloud Run scales from zero and persists data in Cloud SQL while you only pay for active usage. The post shows how to call Gemini as the agent LLM and optionally connect workflows to Google Workspace via OAuth for Gmail, Calendar, and Drive. For production, follow the n8n docs to add Secrets Manager, Cloud SQL, and Terraform-based deployment.

🔍 AlloyDB AI launches two preview features—Auto Vector Embeddings and Auto Vector Index—that let teams convert operational databases into AI-native stores using simple SQL. Auto Vector Embeddings generates and incrementally refreshes vectors in-database, batching calls to Vertex AI and running as a background process. The Auto Vector Index (ScaNN) self-configures, self-tunes, and maintains vector indexes to accelerate filtered semantic search and reduce ETL and tuning overhead for production workloads.

🇪🇺 Google Cloud has formed a new European Advisory Board to provide strategic counsel on regulatory, product, and market priorities and to help customers navigate complex European requirements. The board unites leaders from technology, finance, retail, and public service, chaired by Jim Snabe, and includes Stefan Heidenreich, Nigel Hinshelwood, Christophe Cuvillier and Tim Radford (joining Jan 2026). The group will meet periodically to guide Europe-first product development, policy engagement, and sustainability efforts, reinforcing Google Cloud’s commitment to regional expertise and customer-focused innovation.

🔒 Enterprises continue to struggle with cloud misconfigurations that expose sensitive data, according to recent industry reporting and a Qualys study. The report cites a 28% breach rate tied to cloud or SaaS services over the past year and high misconfiguration rates across AWS (45%), GCP (63%) and Azure (70%). Experts blame permissive provider defaults, shadow IT and rapid business-driven deployments, and recommend controls such as MFA everywhere, private networking, encryption, least-privilege and infrastructure-as-code.

🚀 Google Cloud announced general availability of Ironwood, its seventh-generation TPU, alongside a new family of Arm-based Axion VMs. Ironwood is optimized for large-scale training, reinforcement learning, and high-volume, low-latency inference, with claims of 10x peak performance over TPU v5p and multi-fold efficiency gains versus TPU v6e (Trillium). The architecture supports superpods up to 9,216 chips, 9.6 Tb/s inter‑chip interconnect, up to 1.77 PB shared HBM, and Optical Circuit Switching for dynamic fabric routing. Complementary software and orchestration updates — including Cluster Director, MaxText improvements, vLLM support, and GKE Inference Gateway — aim to reduce time-to-first-token and serving costs, while Axion N4A/C4A instances provide ARM-based CPU options for cost-sensitive inference and data-prep workloads.

🔧 Google Cloud is introducing C4A metal, a bare-metal instance class powered by its Arm-based Axion processors, entering preview soon. Designed for workloads that require direct hardware access and Arm-native compatibility, C4A metal delivers 96 vCPUs, 768 GB DDR5 memory, up to 100 Gbps networking, and support for Google Cloud Hyperdisk variants. C4A metal targets Android development, automotive simulation, CI/CD, security workloads, and custom hypervisors by eliminating nested virtualization overhead and preserving Arm instruction-set parity.

🚀 Google Cloud has introduced the Axion-based N4A VM series in preview, positioned as the most cost-effective N-series to date with up to 2× better price-performance and 80% better performance-per-watt versus comparable x86 VMs. Available on Compute Engine, GKE, Dataproc and Batch, N4A supports up to 64 vCPUs, 512 GB DDR5, 50 Gbps networking, Custom Machine Types and new Hyperdisk storage profiles (Balanced, Throughput, ML). Early customers report substantial cost and performance gains.

🚀 The Ironwood TPU stack is a co‑designed hardware and software platform that scales from massive pre‑training to low‑latency inference. It combines dense MXU compute, ample HBM3E memory, and a high‑bandwidth ICI/OCS interconnect with compiler-driven optimizations in XLA and native support for JAX and PyTorch. Pallas and Mosaic enable hand‑tuned kernels for peak performance, while observability and orchestration tools address resilience and efficiency across pods and superpods.

🚀 Vertex AI Agent Builder is Google Cloud's integrated platform to build, scale, and govern production AI agents. The update expands the Agent Development Kit (ADK) and Agent Engine with configurable context layers to reduce token usage, an adaptable plugins framework, and new language SDK support including Go. Production features include observability, evaluation tools, simplified deployment via the ADK CLI, and strengthened governance with native agent identities and Model Armor protections.