https://yennj12.js.org/yennj12_blog_V4/tags/azure/Recent content in Azure on YennJ12 Engineering BlogHugo -- gohugo.ioen-usTue, 26 May 2026 17:01:52 +0900https://yennj12.js.org/yennj12_blog_V4/posts/ai-fde-essential-guide-part3-zh/Tue, 26 May 2026 17:01:52 +0900https://yennj12.js.org/yennj12_blog_V4/posts/ai-fde-essential-guide-part3-zh/前言 企業級 AI 整合與部署是 AI FDE 最具挑戰性的工作之一。需要處理複雜的企業架構、安全合規要求、數據整合與系統可靠性問題。本文將深入探討雲端平台部署策略、企業安全框架、RAG 架構設計與數據管道建構等核心技術。 1. 雲端平台部署策略 Google Cloud Platform (GCP) 深度整合 Vertex AI 生產部署： 1from google.cloud import aiplatform 2from google.cloud.aiplatform import gapic 3import yaml 4 5class GCPAIDeploymentManager: 6 def __init__(self, project_id: str, region: str = "us-central1"): 7 self.project_id = project_id 8 self.region = region 9 10 # 初始化 Vertex AI 11 aiplatform.init( 12 project=project_id, 13 location=region, 14 staging_bucket=f"gs://{project_id}-ml-staging" 15 ) 16 17 def deploy_custom_model(self, model_config: dict): 18 """部署客製化模型到 Vertex AI""" 19 20 # 創建容器映像 21 container_spec = { 22 "image_uri": model_config["container_image"], 23 "env": [ 24 {"name": "MODEL_NAME", "value": model_config["model_name"]}, 25 {"name": "MODEL_VERSION", "value": model_config["version"]}, 26 {"name": "BATCH_SIZE", "value": str(model_config.