All news with #google cloud tag

🤖 Google Cloud Marketplace offers a vetted path for organizations to deploy, buy, or build AI agents that integrate with Gemini Enterprise, simplifying procurement and deployment. Listings are pre-validated for A2A integration and allow consolidated billing, while administrators can enforce governance using IAM and Private Marketplace controls. For partners, the Marketplace provides global reach, co-selling, and flexible monetization (subscription, usage-based, private offers, outcome-based) plus automated provisioning via Pub/Sub and the Cloud Commerce Partner Procurement API.

🧠 Google Cloud is introducing two Cloud Storage features—auto annotate and object contexts—that apply pretrained AI to generate metadata and attach custom key-value tags to stored objects. Auto annotate (experimental) produces image annotations such as object detection, labels, and objectionable-content signals tied to an object's lifecycle. Object contexts (preview) let teams add, manage, and query contextual tags with IAM controls and Storage Insights integration. Together they enable scalable discovery, curation, and governance of previously unanalyzed unstructured “dark data.”

🔒 BigQuery data clean room query templates are now available in preview, enabling clean room owners to publish fixed, reusable TVF-based queries that accept table or field inputs and return only aggregated rows. Templates reduce data exfiltration risk, simplify onboarding for non-SQL users, and enforce consistent analytical and privacy controls via aggregation thresholds and approval workflows. They support single-direction and multi-party collaboration while keeping query logic hidden from subscribers.

⚙️ This post introduces the fundamentals of chaos engineering and explains why deliberately injecting controlled failures helps teams build more resilient cloud-native systems. It covers core principles — such as defining a steady-state hypothesis, limiting blast radius, replicating realistic failure modes, and automating experiments — and translates them into practical steps for experiment design, fault injection, probing, and rollback. The article recommends using Chaos Toolkit and points to Google Cloud–specific recipes to help engineers begin safely and iteratively.

🔒 Google Cloud has launched a Network Security Learning Path culminating in the Designing Network Security in Google Cloud skill badge to help organizations secure dynamic cloud networks. The program covers design, build, and management of secure VPCs, GKE lockdown, NGFW rules, Cloud VPN/Interconnect, and Cloud Armor for WAF and DDoS protection. Learners validate skills through a hands-on break-fix challenge lab simulating incidents like firewall policy breaches and data exfiltration.

🤖 Google Cloud published the Startup technical guide: AI agents, a practical, operations-driven roadmap to design, build, and operate agentic systems for startups. The guide outlines three paths — build with the open-source Agent Development Kit (ADK), design no-code agents in Agentspace, or adopt managed and partner agents via Vertex AI and the Agent Garden marketplace. It details four development steps (identity, prime directive, tools, lifecycle), highlights operational rigor (AgentOps), and promotes interoperability through standards such as MCP and A2A, all aimed at safe production deployment.

🔐 Google Cloud Key Management Service (Cloud KMS) now offers preview support for post-quantum Key Encapsulation Mechanisms (KEMs), enabling customers to begin migrating to quantum-resistant key exchange. Cloud KMS supports ML-KEM-768, ML-KEM-1024, and the hybrid X-Wing (X25519+ML-KEM-768) option. The preview aims to mitigate "Harvest Now, Decrypt Later" risks and provide HPKE-compatible integrations via Tink and BoringCrypto. Developers are advised to adopt hybrid deployments and plan for larger key and ciphertext sizes that affect bandwidth and storage.

🤖 At the AI Builders Forum, Google Cloud highlighted 150 startups using its generative AI stack—Vertex AI, Gemini, GKE, and Cloud Storage—to build agentic systems, healthcare models, developer tools, and media pipelines. The post catalogs companies across sectors (healthcare, finance, retail, security, creative) and describes technical integrations such as fine-tuning with Gemini, inference on GKE, and scalable analytics with BigQuery. It encourages startups to join Google for Startups Cloud and references a new Startup Technical Guide: AI Agents for building and scaling agentic applications.

🔐 Zeroday Cloud is a new hacking competition focused on open-source cloud and AI tools, offering a $4.5 million bug bounty pool. Hosted by Wiz Research with Google Cloud, AWS, and Microsoft, it takes place December 10–11 at Black Hat Europe in London. The contest features six categories covering AI, Kubernetes, containers, web servers, databases, and DevOps, with bounties ranging from $10,000 to $300,000. Participants must deliver complete compromises and register via HackerOne.

💡 Google Cloud presents practical strategies to lower Compute Engine and block storage costs during migration and modernization. The article recommends adopting latest-generation VMs and specialized instance families, right-sizing or using custom machine types, and tuning storage with Hyperdisk and storage pools to align capacity and performance. It also emphasizes financial levers—committed use discounts, Spot VMs, autoscaling, and recommender-driven actions—to reduce spend while preserving performance.

🔐 Google Cloud’s Vertex AI now supports secure self-deployment of proprietary third-party models directly into customer VPCs via the Model Garden. Customers can discover, license, and deploy closed-source and restricted-license models from partners such as AI21 Labs, Mistral AI, Qodo and others, with one-click provisioning and managed inference. Deployments adhere to VPC-SC controls, selectable regions, autoscaling, and pay-as-you-go billing. This central catalog brings Google, open, and partner models together for enterprise-grade control and compliance.

🎨Google Cloud announced General Availability and feature updates for its generative media models on Vertex AI, including Gemini 2.5 Flash Image, Veo 3, Imagen 4, and Gemini 2.5 TTS. The release emphasizes production readiness and enterprise security while adding multi‑aspect ratio image generation, batch image processing, vertical 9:16 video formats with precise duration controls, and studio‑quality multi‑speaker text‑to‑speech across 70+ languages. These enhancements target teams seeking faster, controlled, and scalable cross‑format media workflows for sight, sound, and motion.

🚀 Google Cloud is hosting a regional event series across EMEA to help developers and tech practitioners learn to build and scale AI agents. The program combines immersive, hands-on labs and expert-led workshops covering technologies such as Cloud Run, Vertex AI, Gemini, and the Agent Development Kit (ADK). Participants receive step-by-step guidance and practical exercises designed to accelerate agent deployments and operational readiness within organizations.

🔒 DZ BANK and Google Cloud present a blueprint for delivering trustworthy off‑chain data to smart contracts, addressing a key barrier to enterprise DLT adoption. The design pairs Google Cloud secure global infrastructure with DZ BANK’s deterministic financial protocols to guarantee data correctness at source, integrity in transit, and timely delivery. The Smart Derivative Contract (SDC) use case demonstrates deterministic valuation, automated margining, and cryptographic attestation of oracle outputs. Production controls such as Binary Authorization, Private Service Connect, Confidential Space (TEE), and TLS are used to mitigate software supply‑chain, transport, and runtime threats.

⚙️ Google Cloud outlines a practical approach to adopt Gen4 VM families by pairing GKE compute classes with Compute Flexible CUDs, enabling prioritized machine-family fallbacks and spend-based discounts. Compute classes let teams define prioritized machine families (for example, N4 then N2) so the cluster autoscaler can provision preferred hardware while preserving availability. Flex CUDs apply discounts across eligible VM families and follow consumption, protecting committed discounts when fallbacks occur. Together these features reduce migration risk and simplify platform operations.

🚀 This roundup highlights how leading organizations are using Google Cloud to optimize networks, accelerate AI, and scale mission-critical services. From Uber reducing edge latency with Hybrid NEGs to Target rebuilding search with AlloyDB AI hybrid search, customers report measurable gains in performance, cost, and reliability. Healthcare, finance, media, and telecommunications teams also describe operational wins — faster inference, seamless migrations, and stronger real-time experiences.

🚀 The U.S. Air Force, working with Google Public Sector and GDIT, deployed the Google Distributed Cloud air-gapped appliance to run classified workloads at the tactical edge in DDIL environments. The rugged, transportable system demonstrated secure, Zero Trust-capable processing up to Secret, delivering on-device AI for transcription, OCR, translation, and summarization during Mobility Guardian 2025 in Guam. It also supported containerized IL2 collaboration, Luna AI integration for low-latency air-defense data, a Jupyter-based edge dev environment, and AI-enabled tele-maintenance to convert manuals and visual data into actionable maintenance insights.

🚀 Google Cloud has launched Flex-start VMs, a Compute Engine consumption option powered by Dynamic Workload Scheduler that lets single VM instances wait in a managed queue for scarce GPUs. By setting request-valid-for-duration (90 seconds–2 hours) users keep capacity requests in PENDING and avoid custom retry logic. Flex-start VMs offer significant discounts versus on‑demand SKUs, consume preemptible quota, run uninterrupted for up to seven days, and integrate directly via the instances.insert API, gcloud CLI, or the Cloud Console. Instances support stop/start to pause billing and an instanceTerminationAction = STOP to preserve configuration when the seven‑day runtime expires.

🚀 Indiana Department of Transportation built a week-long pilot on Google Cloud to meet a 30-day executive order, using a Retrieval-Augmented Generation workflow that combined rapid ETL, Vertex AI Search indexing, and Gemini. The system scraped and parsed decades of internal policies and manuals, produced draft reports across nine divisions with 98% fidelity, and saved an estimated 360 hours of manual effort, enabling INDOT to submit on time.

🤖 DB Lumina is Deutsche Bank Research’s AI-powered assistant, built on Google Cloud and integrating multimodal Gemini models, RAG retrieval, and vector search. It provides a conversational chat interface, reusable prompt templates, and document-grounded answers with inline citations and enterprise guardrails for compliance. Early deployment to roughly 5,000 analysts has yielded measurable time savings, deeper analysis, and improved editorial accuracy.