All news with #vertex ai tag

🔒 Gemma 4 is now available on Google Cloud as an open, commercially permissive (Apache 2.0) family of models with context windows up to 256K, native vision and audio processing, and support for over 140 languages. Enterprises can deploy and fine-tune Gemma 4 via Vertex AI, serve inference serverlessly on Cloud Run, or run production workloads on GKE and TPUs. The release highlights data residency and compliance through Sovereign Cloud options and open weights, enabling secure, controlled AI deployments.

🔒 Unit 42 disclosed a permissions flaw in Vertex AI where the default Per-Project, Per-Product Service Agent (P4SA) can expose credentials and OAuth scopes via the metadata service. Researchers showed attackers could use those credentials to pivot into customer projects, read Google Cloud Storage buckets, and download images from restricted Artifact Registry repositories. Google updated docs and advises using BYOSA and least-privilege scopes; organizations should validate agent permissions before deployment.

🔒 Unit 42 researchers discovered that AI agents deployed with Google Cloud’s Vertex AI ADK can inherit overly broad default permissions, enabling a deployed agent to leak service‑agent credentials and act as a “double agent.” By exploiting the Per‑Project, Per‑Product Service Agent (P4SA), the team pivoted into consumer projects and downloaded restricted Artifact Registry images from Google‑managed producer projects. Google collaborated with Unit 42, updated documentation, and recommended Bring Your Own Service Account (BYOSA) as a mitigation. Palo Alto Networks highlights protection via Prisma AIRS, Cortex Cloud Identity Security, and Cortex AI‑SPM.

🤖 Dev Signal is a multi-agent system designed to turn raw community signals into reliable technical guidance by automating the path from trend discovery to expert content creation. It relies on the Model Context Protocol (MCP) to standardize integrations with Reddit, Google Cloud Docs, and a custom Nano Banana Pro MCP server, all coordinated by a Root Orchestrator that manages three specialist agents. A dual-layer memory model uses Vertex AI for long-term embeddings while the Session Service preserves short-term state, with automated callbacks and tools (save_session_to_memory_callback, PreloadMemoryTool, LoadMemoryTool) to persist and fetch user preferences and stylistic signals.

⚡ Karl Weinmeister frames LLM inference as an efficient frontier that trades latency against throughput and argues production systems often sit below this curve. He presents five actionable optimizations—semantic model routing, prefill/decode disaggregation, modern quantization, context-aware L7 routing with prefix caching, and speculative decoding—and explains their practical tradeoffs. A Vertex AI case study reports 35% faster time-to-first-token and doubled prefix cache hit rates after deploying GKE Inference Gateway.

🔒 Google Cloud’s Sensitive Data Protection (SDP) now applies advanced AI context classifiers and image object detectors to identify and redact sensitive content across text and images. It detects medical and financial contexts, faces, passports, credit cards, and other PII, and can generate redacted versions so organizations keep valuable training data while protecting privacy. SDP supports both Vertex AI tuning and live agent interactions and integrates with Model Armor, Security Command Center, and contact center solutions.

⚠️ Building LLM applications on Vertex AI can trigger 429 errors when request rates exceed available throughput, degrading user experience and increasing retries. This article explains consumption options—Standard and Priority PayGo, Provisioned Throughput, Flex PayGo, and Batch—and prescribes five operational practices: smart retries, global model routing, context caching, prompt optimization, and traffic shaping. Combining these approaches (for example PT for critical real-time traffic and Batch for latency-tolerant jobs) helps preserve performance and control costs.

🚗 BMW Group and Google Cloud present a proof-of-concept pipeline to compress, fine-tune, evaluate, and deploy domain-specific small language models (SLMs) for in-vehicle voice commands. They position SLMs as a practical compromise between full cloud-based LLMs and constrained onboard hardware, reducing latency and network dependence. Using Vertex AI Pipelines, the automated workflow explores quantization, pruning, distillation, LoRA fine-tuning, and RL-based alignment, and validates models on Android/AOSP head-unit environments. The team publishes the pipeline code to encourage reuse and reproducible experimentation.

🚀 At MWC Barcelona, Google Cloud outlines a shift from AI-driven insights to agentic telco operations, showcasing tools that embed AI into network control to achieve Level 4–5 autonomy. The company highlights a dynamic network digital twin, a unified graph data layer using Spanner Graph and BigQuery, and real-time GNN predictions in Vertex AI. New open-source telco data pipelines and two proof-of-value agents — a data steward and autonomous network agents — aim to accelerate trials and reduce legacy bottlenecks.

🖼️ Nano Banana 2 is Google’s latest image-generation and editing model, delivering Pro-level image quality and fast iteration for enterprise creative workflows. Powered by real-time web search and integrated with Gemini API in Vertex AI, it provides accurate, localized visuals plus premium features like text rendering, translations, and upscaling to 2K/4K. Enterprise-ready provenance is supported via SynthID and interoperable C2PA Content Credentials to surface how AI was used.

🚀 Google announced Gemini 3.1 Pro, an upgraded foundation model in the Gemini 3 series that emphasizes deeper reasoning and complex problem solving. The model is available in preview in Vertex AI and Gemini Enterprise, and developers can access it through Google AI Studio, the Gemini API, Android Studio, Google Antigravity, and the Gemini CLI. Early customers report meaningful gains in speed, efficiency, and accuracy across code, 3D transformations, and product design workflows.

⚙️ Provisioned Throughput on Vertex AI standardizes reserved capacity across first-party, third-party, and open-source models, adding multimodal and operational enhancements to support production-scale AI agents. The update introduces Anthropic integration (private preview), PT for popular open models such as Llama 4, Qwen3, and GLM-4.7, and native support for high-bandwidth modalities including Gemini 3, Nano Banana, and Gemini Live API. Operational improvements — one-week PT terms, scheduled change orders, and explicit caching for long contexts — enable predictable latency, flexible commitments, and lower input costs for peak events and high-concurrency workloads.

🔧 This guide explains how to adapt foundation models on Google Cloud by fine-tuning both managed and self-managed workflows. It contrasts a fully managed Vertex AI Supervised Fine-Tuning path for models like Gemini with a customizable GKE approach using LoRA on open-source models such as Llama. The labs walk through data preparation, baseline evaluation, tuning, and automated evaluation metrics, as well as GKE infrastructure, GPU provisioning, security with Workload Identity, and containerized training for production readiness.

🧰 The Google Cloud samples team describes building a specialized end-to-end system that uses Gemini on Vertex AI and Genkit to produce production-ready educational code samples across many languages and products. Their architecture separates generation, validation, and delivery so LLM outputs are combined with deterministic automations, linters, unit tests, and human review. The post presents seven practical technical takeaways—decomposition, determinism, precise prompts, vetted evaluation, scaled downstream processes, end-to-end testing, and solid engineering practices—that drove reliable, scalable sample generation.

🚀 The Vertex AI team deployed the GKE Inference Gateway, built on the Kubernetes Gateway API, to reduce inference latency and improve cache efficiency without a custom scheduler. The gateway applies load-aware routing—scraping Prometheus metrics like KV cache utilization and queue depth—and content-aware routing that inspects request prefixes to send traffic to pods with warm context. In production this cut Time to First Token by ~35% for Qwen3-Coder, improved P95 by ~52% for a bursty chat model, and doubled prefix-cache hit rates from 35% to 70%.

🚀 Google Cloud has added Anthropic's Claude Opus 4.6 to Vertex AI, extending its curated model catalog for enterprise and agentic workloads. Opus 4.6 is positioned for complex coding, polished document and spreadsheet generation, advanced tool calling, and sophisticated multi-step agents. Feature highlights include GA support for adaptive thinking, an effort parameter, 128k output tokens, and previews for a 1M context window and compaction API. Google emphasizes managed agent tooling, governance, and infrastructure to deploy Claude-powered agents at scale.

🚀 Google Cloud is launching a two-day roadshow across North America focused on building production-grade and multimodal AI systems. Day 1, the Production-Ready AI Intensive, covers stability, security, and scalable architecture including multi-agent orchestration with the Agent Development Kit (ADK), A2A protocols on Cloud Run, automated evaluation via the Vertex AI Gen AI Evaluation SDK, and defenses like Model Armor and Sensitive Data Protection. Day 2, the Multimodal Frontier, is a hands-on, code-first workshop on real-time perception and interaction: simultaneous audio/video processing, Graph RAG with Spanner Graph, Persistent Memory Banks, and the Gemini Live API for zero-latency, interruptible agents. Sessions include labs, credits, and networking; seats are limited.

🔧 This guide explains how to build custom employee onboarding agents using the Agent Development Kit (ADK), Vertex AI Agent Engine, and Application Integration to connect conversational AI with enterprise systems such as ITSM, ERP, and CRM. It describes a grounded agentic workflow where a Gemini Enterprise front-end captures intent, a low-code Application Integration layer performs deterministic transformations and authentication, and backend systems execute transactions. The result is a role-aware, auditable onboarding experience that automates tasks like laptop provisioning while keeping business rules and approvals intact.

🚀 Google.Cloud.VertexAI.Extensions brings Microsoft.Extensions.AI abstractions to .NET developers, enabling access to Google Gemini models on Vertex AI via a unified API. The preview package implements core interfaces — IChatClient, IEmbeddingGenerator, and an experimental IImageGenerator — and supports chat, streaming responses, embeddings, and image generation samples. It targets developers who want provider-agnostic integration and invites feedback while in pre-release.

🚀 Google Cloud has launched a new Bangkok (asia-southeast3) region to deliver low-latency, high-performance cloud services while enabling local data residency under Thailand’s PDPA. The region is part of a USD $1 billion investment and is expected to generate THB 1.4 trillion (US$41 billion) in economic value over five years and support roughly 130,000 jobs annually. It offers certified security controls (ISO/IEC, PCI DSS, SOC), default encryption, customer-managed keys, and direct access to Vertex AI, enterprise Gemini, and generative models to accelerate local AI adoption.