All news with #vertex ai tag

🤖 Seattle Children’s partnered with Google Cloud to build Pathway Assistant, a multimodal AI chatbot that turns thousands of pediatric clinical pathway PDFs into conversational, searchable guidance. Using Vertex AI and Gemini, the assistant extracts JSON metadata, parses diagrams and flowcharts, and returns cited answers in seconds. The tool logs clinician feedback to BigQuery and stores source documents in Cloud Storage, enabling continuous improvement of documentation and metadata.

🤖 Mr. Cooper partnered with Google Cloud to develop CIERA, a modular agentic AI framework that assembles specialized agents to support mortgage servicing representatives and customers. The design assigns distinct roles — orchestration, task execution, data retrieval, memory, and evaluation — while keeping humans in the loop for verification and personalization. Built on Vertex AI, CIERA aims to reduce research time, lower average handling time, and preserve trust and compliance in regulated workflows.

🚀 Google Cloud highlights how its differentiated AI tech stack is accelerating startup innovation worldwide, with nine of the top ten AI labs, most AI unicorns, and more than 60% of generative AI startups using its platform. Startups are leveraging Vertex AI, TPUs, multimodal models like Veo 3 and Gemini, plus services such as AI Studio and GKE to build agents, generative media, medical tools, and developer platforms. Programs like the Google for Startups Cloud Program provide credits, mentorship, and engineering support to help founders scale.

🚀 California is partnering with Google Cloud to modernize state and local services by applying AI, security, and infrastructure solutions. Agencies such as Covered California use Document AI, Assured Workloads, and AI-driven security to speed eligibility decisions and protect sensitive data. Universities including UCR and Caltech are using Vertex AI and AI-optimized HPC for research acceleration. Workspace, Gemini, and Agentspace are cited as productivity and information-management enablers.

📘 Google Cloud has published a new ebook, A Practical Guide to Data Science with Google Cloud, aimed at practitioners adopting an AI-first approach across BigQuery, Vertex AI, and Serverless for Apache Spark. The guide emphasizes unified, streamlined workflows enabled by a central notebook experience that blends SQL, Python, and Spark and includes assistive features in Colab Enterprise to generate multi-step plans and code. It explains how a unified data foundation lets teams manage structured and unstructured data together and use familiar SQL to process documents and images. The ebook also offers real-world use cases with linked notebooks so practitioners can run the examples and accelerate delivery.

🛒 Google Cloud announced general availability of the Conversational Commerce agent on Vertex AI, a shopping-focused conversational assistant designed to guide customers from intent to purchase. The agent uses Gemini to interpret complex queries, supports context retention across sessions and devices, and offers administrative controls to boost, bury, or restrict products. Albertsons Cos. reports increased basket size in early use. Onboarding is presented as quick with minimal development effort via the Vertex AI console.

🔒 Google Cloud and its partners announced a range of partner-built AI security solutions now available in the Google Cloud Marketplace. These integrations embed Gemini and Vertex AI into partner products — including CrowdStrike, Palo Alto Networks, Fortinet, and others — to protect models, data, applications, and agents. The collaborations emphasize automated detection, incident response, DLP, identity protection, and agent monitoring to reduce mean time to detect and respond, helping customers adopt AI securely.

🔍 StreamSight is an AI-driven application developed by BMG in partnership with Google Cloud to improve transparency, speed, and accuracy in digital royalty forecasting and anomaly detection. The solution leverages BigQuery ML models (including ARIMA_PLUS and BOOSTED_TREE), uses Vertex AI and Python for training, and surfaces results in Looker Studio dashboards. It flags missing sales periods, rights mismatches, and sudden streaming spikes to reduce manual review and help accelerate fairer payouts. Currently a proof of concept, StreamSight is positioned for broader DSP integrations and richer data inputs to extend its capabilities.

🔔 This month Google Cloud expanded its AI stack across models, tooling, and security. Highlights include Gemini 2.5 Flash with native image generation and SynthID watermarking on Vertex AI, new Veo video models, the Gemini CLI, and a global Anthropic Claude endpoint. Google also published 101 gen‑AI blueprints, developer guidance for choosing tools, and security advances for agents and AI workloads.

☁️ Google Public Sector has been awarded a $48 million DLA Enterprise Platform contract to migrate the Defense Logistics Agency to a DoD‑accredited commercial cloud. The multi‑phased program will move key infrastructure and data to a modern, AI‑ready Google Cloud foundation and enable BigQuery, Looker, and Vertex AI analytics. Emphasizing secure‑by‑design infrastructure and Mandiant threat intelligence, the effort aims to reduce costs, improve resiliency, and accelerate AI‑driven logistics and transportation management.

🚀 Gemini on Google Distributed Cloud (GDC) is now generally available for customers, bringing Google’s advanced Gemini models on‑premises with GA for air‑gapped deployments and a connected preview. The solution provides managed Gemini endpoints with zero‑touch updates, automatic load balancing and autoscaling, and integrates with Vertex AI and preview agents. It pairs Gemini 2.5 Flash and Pro with NVIDIA Hopper and Blackwell accelerators and includes audit logging, access controls, and support for Confidential Computing (Intel TDX and NVIDIA) to meet strict data residency, sovereignty, and compliance requirements.

🖼️ Google announced native image generation and editing in Gemini 2.5 Flash Image, now available in preview on Vertex AI. The model delivers state-of-the-art capabilities including multi-image fusion, character and style consistency, and conversational editing to refine visuals via natural-language loops. Built-in SynthID watermarking supports responsible, transparent use. Developers and partners report promising integrations and low-latency performance for real-time editing workflows.

⚙️ This guide from Google Cloud authors Eric Hanley and Brittany Rockwell explains how to tune vLLM deployments for xPU inference, covering accelerator selection, memory sizing, configuration, and benchmarking. It shows how to gather workload parameters, estimate HBM/VRAM needs (example: gemma-3-27b-it ≈57 GB), and run vLLM’s auto_tune to find optimal gpu_memory_utilization and throughput. The post compares GPU and TPU options and includes practical troubleshooting tips, cost analyses, and resources to reproduce benchmarks and HBM calculations.