All news with #vertex ai tag

Nano Banana Pro: Gemini 3 Pro Image for Enterprise Use

🎨 Google is unveiling Nano Banana Pro (Gemini 3 Pro Image), a high-fidelity image generation and editing model available today in Vertex AI and Google Workspace, with a rollout to Gemini Enterprise coming soon. The model supports multi-language text rendering and on-image translation, connects to Google Search for context-aware outputs, and accepts up to 14 reference images and 4K inputs for production-grade assets. Built-in SynthID watermarking and planned copyright indemnification address commercial use and responsible deployment.

Google Named Leader in Gartner MQ for AI Platforms

🚀 Google has been named a Leader in the inaugural 2025 Gartner Magic Quadrant for AI Application Development Platforms and ranked highest for Ability to Execute. The announcement highlights Vertex AI as a unified, governed platform that delivers model choice, customization, and production-grade agent capabilities across an enterprise. Key capabilities cited include the Vertex AI Model Garden and Gemini 3, Vertex AI Training, Agent Builder and Agent Engine for multi-agent systems, and operational controls for observability, security, and predictable cost.

BigLake Metastore Adds Iceberg REST Catalog Support

🔔 Google Cloud announced general availability of BigLake metastore support for the Iceberg REST Catalog, offering a serverless, standards-based runtime metastore that enables interoperability across Iceberg-compatible engines (Spark, Trino) and BigQuery. The service provides credential vending, integrated governance via Dataplex Universal Catalog for lineage and data quality, and a UX console for creating and managing Iceberg catalogs. By removing the need to run custom metastore deployments, BigLake metastore aims to reduce operational overhead while preserving enterprise scale and security.

TimesFM Integration Brings Forecasting to BigQuery

🕒 Google is integrating the TimesFM time-series foundation model into BigQuery and AlloyDB, enabling zero-shot forecasting on customer data without retraining. AI.FORECAST and AI.EVALUATE are now Generally Available in BigQuery, while AI.DETECT_ANOMALIES is in public preview. TimesFM 2.5 offers improved accuracy and lower latency, supports dynamic context windows up to 15K, and can return historical data with forecasts. AlloyDB preview lets users call TimesFM endpoints hosted on Vertex AI so operational data can be forecasted in-place, preserving data residency and reducing export overhead.

Gemini 3 Brings Multimodal and Agentic AI to Enterprise

🤖 Google has made Gemini 3 available to enterprises and developers via Gemini Enterprise and Vertex AI, bringing advanced multimodal reasoning and agentic capabilities to production teams. The model can analyze text, images, video, audio, and code together, supports a 1M-token context window, and improves frontend generation, legacy code migration, and long-running tool orchestration. Early partners report faster diagnostics, richer UI prototypes, and more reliable automation across business workflows.

Google Gemini 3 Appears on AI Studio Ahead of Release

🤖 Google’s Gemini 3 has been spotted in AI Studio, suggesting an imminent rollout that could begin within hours or days. The AI Studio entry references how temperature influences reasoning — noting "For Gemini 3, best results at default 1.0. Lower values may impact reasoning" — and highlights controls such as context size and temperature. Earlier sightings on Vertex AI show a preview build named gemini-3-pro-preview-11-2025, while Google is also testing an image model codenamed GEMPIX2 (Nano Banana 2).

Google announces Dhivaru subsea cable and regional hubs

📡 Google announced Dhivaru, a new Trans-Indian Ocean subsea cable linking the Maldives, Christmas Island, and Oman, and will build two new connectivity hubs in the Maldives and Christmas Island. The investment builds on the Australia Connect initiative and aims to improve reach, reliability, and resilience across the Indian Ocean, supporting AI services such as Vertex AI. The hubs will provide cable switching, content caching, and colocation to reduce latency, improve availability, and support local ecosystems.

Production-Ready AI with Google Cloud Learning Path

🚀 Google Cloud has launched the Production-Ready AI Learning Path, a free curriculum designed to guide developers from prototype to production. Drawing on an internal playbook, the series pairs Gemini models with production-grade tools like Vertex AI, Google Kubernetes Engine, and Cloud Run. Modules cover LLM app development, open model deployment, agent building, security, RAG, evaluation, and fine-tuning. New modules will be added weekly through mid-December.

Advancing Text-to-SQL: Gemini's BIRD Benchmark Breakthrough

🚀 Google Cloud reports a new state-of-the-art Single Trained Model Track score on the BIRD benchmark, achieving 76.13 with a fine-tuned Gemini 2.5-pro. The team credits rigorous data filtering, multitask supervised fine-tuning, and test-time self-consistency selection for the gains. These improvements bolster NL2SQL features in AlloyDB AI and BigQuery, and enhance developer tooling such as Gemini Code Assist for reliable SQL generation.

Four Steps for Startups to Build Multi-Agent Systems

🤖 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 expands Hugging Face support for AI developers

🤝 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 Expands AI Infrastructure and Services in India

🤝 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.

AlloyDB AI: Auto Vector Embeddings and Indexing Capabilities

🔍 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.

Build Your First AI Travel Assistant with Gemini Today

🚀 This codelab walks developers through building a functional travel chatbot using Google's Gemini via the Vertex AI SDK. It explains how to connect a web frontend to Gemini, craft system instructions to shape assistant behavior, and enable function-calling to fetch live data such as geocoding and weather. No advanced ML expertise is required; the lab provides step-by-step code samples, API usage, and practical recommendations for iterating prompts so you can produce a working, production-ready demo.

Vertex AI Agent Builder: Build, Scale, Govern Agents

🚀 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.

How Google Cloud Networking Supports AI Workloads at Scale

🔗 Networking is a critical enabler for AI on Google Cloud, connecting models, storage, and inference endpoints while preserving security and performance. The post outlines seven capabilities—from private API access and RDMA-backed GPU interconnects to hybrid Cross-Cloud links—that reduce latency, prevent data exfiltration, and simplify model serving. It also highlights options for exposing inference (managed services, GKE, load balancing) and previews AI-driven network operations using Gemini.

Conversational AI Agents: Designing for Retail UX, Commerce

🛍️ Google Cloud outlines UX and implementation guidance for building conversational AI agents tailored to online shopping. The article presents seven practical design principles — including multimodal input, intelligent query handling, rich visual presentation, and clear trust signals — that improve discovery and reduce friction. It highlights features like predictive assistance and contextual clarification and offers a Figma component library plus developer resources to accelerate deployment.

Giles AI on Google Cloud: Transforming Medical Research

🚀 Giles AI migrated its healthcare-focused platform to Google Cloud to reduce latency, improve scalability, and accelerate developer velocity. Using Google Kubernetes Engine, Cloud Run, and Compute Engine, the company orchestrates complex clinical data flows and routes prompts through Vertex AI and Model Garden to remain model-agnostic. Data storage and extraction are handled with Cloud SQL, Cloud Storage, and Document AI, while Cloud Armor and Security Command Center bolster security and compliance. Early customer results include dramatic reductions in research time and improvements in response accuracy.

A4X Max, GKE Networking, and Vertex AI Training Now Shipping

🚀 Google Cloud is expanding its NVIDIA collaboration with the new A4X Max instances powered by NVIDIA GB300 NVL72, delivering 72 GPUs with high‑bandwidth NVLink and shared memory for demanding multimodal reasoning. GKE now supports DRANET for topology‑aware RDMA scheduling and integrates NVIDIA NeMo Guardrails into GKE Inference Gateway, while Vertex AI Model Garden will host NVIDIA Nemotron models. Vertex AI Training adds NeMo and NeMo‑RL recipes and a managed Slurm environment to accelerate large‑scale training and deployment.

Enabling a Safe Agentic Web with reCAPTCHA Controls

🔐 Google Cloud outlines a pragmatic framework to secure the emerging agentic web while preserving smooth user experiences. The post details how reCAPTCHA and Google Cloud combine agent and user identity, continuous behavior analysis, and AI-resistant mitigations such as mobile-device attestations. It highlights enabling safe agentic commerce via protocols like AP2 and tighter integration with cloud AI services.