- 精通 Python 程式設計
- 了解機器學習工作流程
- 熟悉容器化工具，如 Docker
- 具有 DevOps 種實踐經驗（推薦）
觀眾
- MLOps 工程師
- DevOps 專業人士
將 AI 代理部署到生產環境中是操作 AI 模型並確保其在實際應用程式中的可擴充性、可靠性和性能的關鍵步驟。
這種講師指導的現場培訓（在線或現場）面向希望掌握在生產環境中部署和管理 AI 代理的技術的高級專業人員。
在本次培訓結束時，參與者將能夠：
- 設計和實施可擴展的 AI 部署管道。
- 使用 Docker 和 Kubernetes 等工具容器化和編排 AI 代理。
- 監控和優化生產環境中的 AI 代理。
- 為 AI 代理部署實施 CI/CD 工作流。
- 確保符合安全和數據管理要求。
課程形式
- 互動講座和討論。
- 大量的練習和練習。
- 在即時實驗室環境中動手實施。
課程自定義選項
- 要申請本課程的定製培訓，請聯繫我們進行安排。
AI 部署簡介
- AI 部署生命週期概述
- 將 AI 代理部署到生產環境的挑戰
- 關鍵注意事項：可伸縮性、可靠性和可維護性
容器化和編排
- Docker 和容器化基礎知識簡介
- 使用 Kubernetes 進行 AI 代理編排
- 管理容器化 AI 應用程式的最佳實踐
提供 AI 模型
- 模型服務框架概述（例如，TensorFlow Serving、TorchServe）
- 構建用於 AI 代理推理的 REST API
- 處理批量預測與實時預測
AI Agents 的 CI/CD
- 為 AI 部署設置 CI/CD 管道
- 自動化 AI 模型的測試和驗證
- 滾動更新和管理版本控制
監控和優化
- 實施 AI 代理性能的監控工具
- 分析模型漂移和重新訓練需求
- 優化資源利用率和可擴充性
安全和 Go生存
- 確保遵守數據隱私法規
- 保護 AI 部署管道和 API
- AI 應用程式的審計和日誌記錄
動手實踐 Activities
- 使用 Docker 容器化 AI 代理
- 使用 Kubernetes 部署 AI 代理
- 設置 AI 性能和資源使用情況監控
總結和後續步驟
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