All news with #cloud run tag

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.

Hands-on with Gemma 3: Deploying Open Models on GCP

🚀 Google Cloud introduces hands-on labs for Gemma 3, a family of lightweight open models offering multimodal (text and image) capabilities and efficient performance on smaller hardware footprints. The labs present two deployment paths: a serverless approach using Cloud Run with GPU support, and a platform approach using GKE for scalable production environments. Choose Cloud Run for simplicity and cost-efficiency or GKE Autopilot for control and robust orchestration to move models from local testing to production.

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.

Deploy n8n on Cloud Run for Serverless AI Workflows

🚀 Deploy the official n8n Docker image to Cloud Run in minutes to run scalable, serverless AI workflows. Cloud Run scales from zero and persists data in Cloud SQL while you only pay for active usage. The post shows how to call Gemini as the agent LLM and optionally connect workflows to Google Workspace via OAuth for Gmail, Calendar, and Drive. For production, follow the n8n docs to add Secrets Manager, Cloud SQL, and Terraform-based 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.

Agent Factory Recap: Securing AI Agents in Production

🛡️ This recap of the Agent Factory episode explains practical strategies for securing production AI agents, demonstrating attacks like prompt injection, invisible Unicode exploits, and vector DB context poisoning. It highlights Model Armor for pre- and post-inference filtering, sandboxed execution, network isolation, observability, and tool safeguards via the Agent Development Kit (ADK). The team demonstrates a secured DevOps assistant that blocks data-exfiltration attempts while preserving intended functionality and provides operational guidance on multi-agent authentication, least-privilege IAM, and compliance-ready logging.

Design Patterns for Scalable AI Agents on Google Cloud

🤖 This post explains how System Integrator partners can build, scale, and manage enterprise-grade AI agents using Google Cloud technologies like Agent Engine, the Agent Development Kit (ADK), and Gemini Enterprise. It summarizes architecture patterns including runtime, memory, the Model Context Protocol (MCP), and the Agent-to-Agent (A2A) protocol, and contrasts managed Agent Engine with self-hosted options such as Cloud Run or GKE. Customer examples from Deloitte and Quantiphi illustrate supply chain and sales automation benefits. The guidance highlights security, observability, persistent memory, and model tuning for enterprise readiness.

Scaling Customer Experience with AI on Google Cloud

🤖 LiveX AI outlines a Google Cloud blueprint to scale conversational customer experiences across chat, voice, and avatar interfaces. The post details how Cloud Run hosts elastic front-end microservices while GKE provides GPU-backed AI inference, and how AgentFlow orchestrates conversational state, knowledge retrieval, and human escalation. Reported customer outcomes include a >90% self-service rate for Wyze and a 3× conversion uplift for Pictory. The design emphasizes cost efficiency, sub-second latency, multilingual support, and secure integrations with platforms such as Stripe, Zendesk, and Salesforce.

Getting Started with Chaos Engineering on Google Cloud

⚙️ 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.

Cost-Saving Strategies When Migrating to Google Cloud

💡 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.

Accelerate AI with Agents: EMEA Developer Series and Labs

🚀 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.

Google Cloud launches advanced AI training suite for roles

🚀 Google Cloud announced a new suite of AI training courses for intermediate and advanced learners across technical and non-technical roles. The curriculum covers designing and managing AI infrastructure using GCE and GKE, fine-tuning models like Gemini, serverless inference with Cloud Run, and securing generative AI deployments. Hands-on labs teach building AI agents that securely connect to enterprise databases and rapid prototyping in Google AI Studio. Courses are available on Google Cloud Skills Boost to help learners future-proof their AI skills.

Securing Remote MCP Servers on Google Cloud Platform

🔒 A centralized proxy architecture on Google Cloud can secure remote Model Context Protocol (MCP) servers by intercepting tool calls and enforcing consistent policies across deployments. Author Lanre Ogunmola outlines five core MCP risks — unauthorized tool exposure, session hijacking, tool shadowing, token/theft and authentication bypass — and recommends an MCP proxy (Cloud Run, GKE, or Apigee) integrated with Cloud Armor, Secret Manager, and identity services for access control, secret scanning, and monitoring. The post emphasizes layered defenses including Model Armor for prompt/response screening and centralized logging to reduce blind spots and operational overhead.

Gemini CLI Extensions: Security and Cloud Run Tools

🚀 Google is previewing two Gemini CLI extensions that bring security analysis and Cloud Run deployment directly into your terminal. The security extension introduces /security:analyze to scan local git diffs for issues such as hardcoded secrets, injection flaws, broken access control, and insecure data handling, and returns clear remediation guidance or optional fixes. The Cloud Run extension adds /deploy, a one-command flow to build, containerize, push, and configure services on Cloud Run, returning a public URL and supporting terminal, VS Code agent mode, and Cloud Shell.

Agent Factory Recap: AI, Future Development, Vibe Coding

🛠️ In Episode #6 of the Agent Factory podcast, Keith Ballinger discusses how AI agents and the Gemini CLI are reshaping software development and elevating developers into orchestration and context engineering roles. He demonstrates 'vibe coding' with live demos that produced a command-line markdown viewer in under 15 minutes and highlights open-source projects Terminus and Aether as practical examples. The episode also addresses infrastructure for AI workloads, multi-cloud and edge orchestration, and the growing need for human review in regulated industries.

High-Availability Multi-Regional Services on Cloud Run

🚀 This Cloud Next 2025 talk explains how to build fault-tolerant, multi-region services using Cloud Run, highlighting autoscaling, decoupled control/data planes, and N+1 zonal redundancy. The post previews an upcoming Service Health feature that automates cross-region failover by relying on container readiness probes and minimum-instance settings. It also outlines deployment patterns (global external ALB with Serverless NEGs) and shows a live demo of automated traffic failover.

What’s New in Google Cloud: Releases, Previews, and News

🔔 Google Cloud published a consolidated roundup of product releases and previews from early July through Aug 22, 2025, covering GA launches, public previews, and platform enhancements. Highlights include Earth Engine in BigQuery (GA), Vertex AI embedding scaling, new GKE features for NUMA alignment and swap, expanded NodeConfig controls, and Cloud Run with GPUs. Customers should review the linked documentation, request preview access via account teams where needed, and plan upgrades or migrations accordingly.