Building Production-Ready Open-Weights AI Agents with Gemma, Google ADK, and Cloud Run GPUs

说明与用途

问题：将开源 Gemma 模型迁移到生产环境时面临基础数据、GPU 扩展、安全治理和测试优化等四大难题。解决方案：CloudGuard 案例使用 Google ADK、Cloud Run GPU、vLLM、Firestore、Model Armor、ADK Eval 和 Vertex AI QLoRA，实现安全、可扩展、可测试的生产级部署。

问题

Engineering teams struggle to move open‑weights models like Google’s Gemma 3 into real enterprise production because of grounding, serverless GPU scaling, security & governance, and testing & optimization challenges.

结果

The CloudGuard blueprint demonstrates that an open‑weights Gemma agent can be deployed at enterprise scale on Google Cloud, achieving zero‑idle GPU cost, secure data handling, and performance comparable to a 27B model with only 4B weights plus LoRA.

工作流

• Build a Google ADK agent that runs locally with Ollama and switches to Cloud Run GPUs or Vertex AI in production
• Connect the agent to Cloud Firestore via native Python function‑calling tools for live incident data and GPU FinOps quotas
• Deploy vLLM on Cloud Run Serverless GPUs with Cloud Storage FUSE for zero‑download weight streaming, using short‑term session state and a long‑term memory bank
• Govern the deployment with private Cloud Run IAM service accounts, Model Armor secret redaction, and deterministic RBAC tool policies
• Optimize with ADK Eval CI/CD gates and OpenTelemetry tracing for Google Cloud Trace
• Specialize by fine‑tuning a Gemma 4B model with QLoRA on Vertex AI to match 27B performance at lower latency and memory

证据

Most AI tutorials stop at a basic "Hello World" chatbot running on a laptop. However, when engineering teams try to move open‑weights models like Google’s Gemma 3 into real enterprise production, they immediately hit four hard engineering questions:

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I wrote this article to serve as a complete, end‑to‑end Enterprise Reference Blueprint that answers all four questions using a single, working project (CloudGuard) verified in Google Cloud Shell

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CloudGuard automatically pulls live incident telemetry from Cloud Firestore (get_service_incident), matches symptoms against historical postmortems in its Long-Term Memory Bank (MEM-2026-014), and executes approved mitigations in staging (mitigate_runaway_workload).

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