All news with #vertex ai tag

🚀 Vertex AI Studio now centers developer workflows around Gemini and introduces agents-as-tools to streamline prompt engineering and app creation. The Studio adds three core agent commands — /Prompt, /Evaluate, and /Build — to refine prompts, assess outputs with custom autoraters, and generate working code. Team features include cross-account prompt sharing, version history, and notes. Onboarding is simplified with one-click API keys, an /Ask helper, express mode, and loginless model trials.

🔬 Google Cloud outlines an agentic AI framework to accelerate life sciences R&D by orchestrating specialized, fine-tunable models into modular workflows. It describes four agents—MedGemma for deep literature and data synthesis, TxGemma for in-silico preclinical prediction, Gemini 2.5 Pro as the cognitive orchestrator, and AlphaFold-2 plus docking tools for molecular design. The architecture maps data flows, tooling, and cloud services (Vertex AI, HPC, search) to move from target discovery through iterative Design→Dock→Predict→Refine cycles toward lab-ready lead nomination while preserving version control and compliance.

🎨 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 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.

🚀 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.

🤖 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.

🔍 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.

🚀 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.

🚀 This post explains how an external KV Cache backed by Google Cloud Managed Lustre can accelerate transformer inference and lower costs by offloading expensive prefill compute to I/O. In experiments with a 50K token context and ~75% cache-hit, Managed Lustre increased inference throughput by 75% and cut mean time-to-first-token by 44%. The analysis projects a 35% TCO reduction and up to ~43% fewer GPUs for the same workload, and the article summarizes practical steps: provision Managed Lustre in the same zone, deploy an inference server that supports external caching (for example vLLM), enable o_direct, and tune I/O parallelism.

🚀 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.

🚀 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.

🚀 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.

🔍 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 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.

📡 This recap of the Agent Factory podcast episode, hosted by Annie Wang with guest Ivan Nardini, explains how to evaluate autonomous agents using a practical, full-stack approach. It outlines what to measure — final outcomes, chain-of-thought, tool use, and memory — and contrasts measurement techniques: ground truth, LLM-as-a-judge, and human review. The post demonstrates a 5-step debugging loop using the Agent Development Kit (ADK) and describes how to scale evaluation to production with Vertex AI.

🔎 Moloco’s AI-native retail media platform, integrated with Vertex AI Vector Search on Google Cloud, delivers semantic, real-time ad retrieval and personalized recommendations. The joint architecture uses TPUs and GPUs for model training and scoring while vector search runs efficiently on CPUs, enabling outcomes-based bidding at scale. Internal benchmarks report ~10x capacity, up to ~25% lower p95 latency, and a ~4% revenue uplift. The managed service reduces operational overhead and accelerates time-to-value for retailers.

🛠️ Google Cloud announced General Availability of Prompt Management in the Vertex AI SDK, enabling teams to programmatically create, version, and manage prompts as first-class assets. The capability bridges Vertex AI Studio’s visual prompt design with SDK-driven automation to improve collaboration, reproducibility, and lifecycle control. Enterprise security and compliance are supported via CMEK and VPCSC, and the SDK exposes simple Python methods to create, list, update, and delete prompt resources tied to models such as gemini-2.5-flash. Get started using the documented code examples to centralize prompt governance and scale generative AI workflows.

🎬 This guide introduces Veo 3.1, Google Cloud's improved generative video model available in preview on Vertex AI, and explains how to move beyond "prompt and pray" toward deliberate creative control. It highlights core capabilities—high-fidelity 720p/1080p output, variable clip lengths, synchronized dialogue and sound effects, and stronger image-to-video fidelity. The article presents a five-part prompting formula and detailed techniques for cinematography, soundstage direction, negative prompting, and timestamped scenes. It also describes advanced multi-step workflows that combine Gemini 2.5 Flash Image to produce consistent characters and controlled transitions, and notes SynthID watermarking and certain current limitations.

⚡ Vertex AI context caching saves and reuses precomputed input tokens so developers avoid repeatedly sending and recomputing long contextual content, reducing latency and cost for large-context AI applications. It provides implicit caching — automatic, default, short-lived KV caches (deleted within 24 hours) integrated with Provisioned Throughput — and explicit CachedContent objects that are paid once and then reused at a deep discount with optional CMEK protection. Caches support multimodal inputs and very large context windows.