All news with #google cloud tag

🚀 Vertex AI Training introduces managed features designed for large-scale model development, simplifying cluster provisioning, job orchestration, and resiliency across hundreds to thousands of accelerators. The offering integrates Cluster Director, Dynamic Workload Scheduler, optimized checkpointing, and curated training recipes, including NVIDIA NeMo support. These capabilities reduce operational overhead and accelerate transitions from pretraining to fine-tuning while improving cost and uptime efficiency.

🔗 Google Cloud announced that Cross-Site Interconnect is now generally available, providing a cloud-delivered, transparent layer 2 connectivity overlay for global WANs. The service uses Google’s global backbone to deliver high-bandwidth, on-demand links with a 99.95% SLA, dynamic consumption pricing, and support for MACsec encryption and VLAN-based multi-destination handoffs. It exposes operational metrics via dashboards and APIs so teams can monitor latency, loss, and utilization while abstracting failover and resiliency management.

🔒 Google Cloud's Parameter Manager centralizes application configuration to avoid hard-coded credentials and fragile config files, supporting validated JSON and YAML payloads as well as arbitrary unformatted data. It integrates with Secret Manager using a __REF__ syntax to keep confidential values separate and uses versioned, immutable parameter versions to prevent accidental changes. The post walks through storing an API key in Secret Manager, granting the Parameter Manager IAM principal access, and calling renderParameterVersion from a Node backend. A sample React/Node weather app demonstrates runtime configuration, fallback dummy data, and advanced patterns such as regional parameters and feature rollouts.

🌐 This blog presents a reference architecture for deploying Network Virtual Appliances (NVAs) in a regional hub-and-spoke design using VPC Network Peering. It explains how Google’s Cross-Cloud Network and software-defined global backbone support any-to-any connectivity while preserving regional affinity for latency and data residency. The post details traffic flows and key services such as Cloud Interconnect, HA VPN, Internal Passthrough Network Load Balancers, policy-based routes, and Private Service Connect to integrate managed services and workload VPCs.

🔐 Organizations are rapidly replacing embedded API keys and passwords with platform-native managed identities to reduce manual credential management and leakage risk. Enterprises report significant productivity gains—case studies cite up to a 95% reduction in time spent managing credentials and a 75% drop in time learning platform authentication. While major clouds (AWS, Azure, GCP) and CI platforms have built-in solutions, legacy systems and third-party APIs remain the primary obstacles to eliminating static secrets entirely.

⚡ Google Cloud announced the H4D VM family (Preview), powered by 5th Gen AMD EPYC processors (Turin), aimed at delivering extreme performance for financial services workloads. The H4D series targets latency-sensitive use cases such as high-frequency trading, Monte Carlo risk simulations, backtesting, and derivatives pricing by offering faster core-to-core communication, larger memory capacity, and improved network throughput. AMD benchmarking with the open-source KX Nano test reported an average ~34% out-of-the-box performance gain over prior C3D VMs, with per-core and multi-threaded uplifts around 1.33–1.36x. Google Cloud will demonstrate H4D and complementary HPC solutions at STAC Summit NYC on October 28th and will have experts available to discuss performance, security, and compliance.

🔒 Google Cloud’s Model Armor integrates with Apigee to screen prompts, responses, and agent interactions, helping organizations mitigate prompt injection, jailbreaks, sensitive data exposure, malicious links, and harmful content. The model‑agnostic, cloud‑agnostic service supports REST APIs and inline integrations with Apigee, Vertex AI, Agentspace, and network service extensions. The article provides step‑by‑step setup: enable the API, create templates, assign service account roles, add SanitizeUserPrompt and SanitizeModelResponse policies to Apigee proxies, and review findings in the AI Protection dashboard.

🔍 Dataplex Universal Catalog now captures column-level lineage for BigQuery, extending object-level tracing to granular column transformations at no extra cost. The update provides interactive visual lineage graphs so users can inspect upstream and downstream flows for individual columns, trace origins, and assess downstream impact of modifications. This granularity helps validate authoritative sources for AI/ML features, enforce column-level governance, and improve compliance. It also surfaces freshness and usage metadata to support context-aware agents.

🛡️ Cloud Armor introduces hierarchical security policies and organization-scoped address groups to simplify centralized policy management across organization, folder, and project levels. The release also includes GA support for JA4 network fingerprinting and ASN/NTI controls for Media CDN, while an enhanced WAF request-body inspection (preview) expands inspection from 8 KB to 64 KB. These updates are designed to strengthen threat protection and reduce operational complexity for hybrid and multicloud deployments.

🔍 SmarterX uses Google Cloud to build custom LLMs that help retailers, manufacturers, and logistics companies manage regulatory compliance across product lifecycles. Using BigQuery, Cloud Storage, Gemini, and Vertex AI, the company ingests, normalizes, and indexes unstructured regulatory and product data, applies RAG and grounding, and trains customer-specific models. The integrated platform empowers subject matter experts to evaluate, correct, and deploy model updates without heavy engineering overhead.

🔧 Google details how its custom Axion Arm CPUs and a mix of automation and AI enabled large-scale migration from x86 to multi-architecture production across services such as YouTube, Gmail, and BigQuery. The team analyzed 38,156 commits (about 700K changed lines) and reports migrating more than 30,000 applications to Arm while keeping both Arm and x86 in production. Existing automation like Rosie, sanitizers, fuzzers, and the CHAMP rollout framework handled much of the work, while an LLM-driven agent called CogniPort fixed build and test failures, showing a 30% success rate on a 245-commit benchmark. Google plans to default new apps to multiarch and continue refining AI tools to address the remaining long tail.

🚀 The G4 VM is now generally available on Google Cloud, powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs and offering up to 768 GB of GDDR7 memory per instance class. It targets latency-sensitive and regulated workloads for generative AI, real-time rendering, simulation, and virtual workstations. Features include FP4 precision support, Multi-Instance GPU (MIG) partitioning, an enhanced PCIe P2P interconnect for faster multi‑GPU All-Reduce, and an NVIDIA Omniverse VMI on Marketplace for industrial digital twins.

🔐 Dataproc 2.3 on Google Compute Engine provides a streamlined image that includes only the essential core components for Spark and Hadoop, reducing the attack surface and simplifying compliance. The image is FedRAMP High compliant and leverages both automated CVE remediation and manual engineering intervention for complex fixes. Optional tools like Flink, Hudi, Ranger, and Zeppelin are available on-demand during cluster creation, or can be pre-baked into custom images to speed provisioning while preserving the security benefits of the lightweight base.

🔧 Google Cloud’s Q3 AI Hypercomputer update highlights inference improvements and expanded tooling to accelerate model serving and diagnostics. The release integrates vLLM with Cloud TPUs via the new tpu-inference plugin, unifying JAX and PyTorch runtimes and boosting TPU inference for models such as Gemma, Llama, and Qwen. Additional launches include improved XProf profiling and Cloud Diagnostics XProf, an AI inference recipe for NVIDIA Dynamo, NVIDIA NeMo RL recipes, and GA of the GKE Inference Gateway and Quickstart to help optimize latency and cost.

🏆 Google Cloud announced it was named a Leader in the 2025 IDC MarketScape for Worldwide GenAI Life-Cycle Foundation Model Software, spotlighting the Gemini model family and the Vertex AI platform. The post highlights Gemini 2.5’s expanded “thinking” capabilities and new cost controls such as thinking budgets and thought summaries for improved auditability. It also underscores native multimodality, creative variants like Nano Banana, developer tooling including the Gemini CLI, and enterprise features for customization, grounding, security, and governance.

🚀 Google Cloud's newly GA G4 VMs combine NVIDIA RTX PRO 6000 Blackwell GPUs with a custom, software-defined PCIe fabric to enable high-performance peer-to-peer (P2P) GPU communication. The platform accelerates collective operations like All-Gather and All-Reduce without code changes, delivering up to 2.2x faster collectives. For tensor-parallel inference, customers can see up to 168% higher throughput and up to 41% lower inter-token latency. G4 integrates with GKE Inference Gateway for horizontal scaling and production deployments.

🤖 This post explains how System Integrator partners can build, scale, and manage enterprise-grade AI agents using Google Cloud technologies like Agent Engine, the Agent Development Kit (ADK), and Gemini Enterprise. It summarizes architecture patterns including runtime, memory, the Model Context Protocol (MCP), and the Agent-to-Agent (A2A) protocol, and contrasts managed Agent Engine with self-hosted options such as Cloud Run or GKE. Customer examples from Deloitte and Quantiphi illustrate supply chain and sales automation benefits. The guidance highlights security, observability, persistent memory, and model tuning for enterprise readiness.

🔍 The Oklahoma Employment Security Commission modernized its 40‑year mainframe data architecture with a cloud-first data platform built on BigQuery and analytics delivered via Looker. Partnering with Google Public Sector and Phase2, OESC reorganized opaque, mainframe-mimicking schemas into a performant, intuitive model and enabled point-in-time snapshots previously impossible. Critical reporting moved from months to hours, stakeholders gained self-service access, and the agency unlocked employer insights that supported tax analysis, improved auditability, and accelerated fraud detection.

🛠️ Google Cloud demonstrates how the Gemini CLI and GKE Inference Quickstart integrate via the Model Context Protocol (MCP) to streamline selecting, benchmarking, and deploying LLMs on GKE. The post outlines installation steps, example prompts to discover cost and performance trade-offs, and how manifests can be generated for target accelerators. This approach reduces manual tuning and provides data-driven recommendations to optimize cost-per-token while preserving performance.

🚀 Google has made Axion-powered C4A and Intel-based N4 virtual machines generally available for Cloud SQL Enterprise Plus and Enterprise editions, promising substantial gains in throughput and price-performance. Hyperdisk Balanced storage is supported on both families to boost I/O, increase throughput, and allow independent configuration of capacity, throughput, and IOPS. Customer tests report lower costs, reduced latency, and large throughput gains. These machines are available in select regions; check Cloud SQL pricing and region documentation for details.