Course Code: dfinetunedmp
Duration: 21 hours
Prerequisites:
  • 對機器學習工作流程有深入的理解
  • 微調 ML 模型的經驗
  • 熟悉 DevOps 或 MLOps 原則

觀眾

  • DevOps 名工程師
  • MLOps 從業者
  • AI 部署專家
Overview:

Deploying Fine-Tuned Models in Production 重點介紹將微調模型集成到可擴展的實際應用程式中所需的實用策略和工具。參與者將學習優化模型的性能、確保監控和安全性，以及解決生產環境中的挑戰。

這種由講師指導的現場培訓（在線或現場）面向希望可靠、高效地部署微調模型的高級專業人員。

在本次培訓結束時，參與者將能夠：

  • 瞭解將微調模型部署到生產環境中所面臨的挑戰。
  • 使用 Docker 和 Kubernetes 等工具容器化和部署模型。
  • 對已部署的模型實施監控和日誌記錄。
  • 在實際場景中優化模型以實現延遲和可擴充性。

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。
Course Outline:

生產部署簡介

  • 部署微調模型的主要挑戰
  • 開發環境和生產環境之間的差異
  • 用於模型部署的工具和平臺

準備用於部署的模型

  • 以標準格式（ONNX、TensorFlow SavedModel 等）導出模型
  • 優化模型的延遲和輸送量
  • 在邊緣案例和真實數據上測試模型

用於模型部署的容器化

  • Docker 簡介
  • 為 ML 模型建立 Docker 個圖像
  • 容器安全性和效率的最佳實踐

使用 Kubernetes 擴展 Deployment

  • 適用於 AI 工作負載的 Kubernetes 簡介
  • 為模型託管設置 Kubernetes 集群
  • 負載均衡和水平擴展

模型監控與維護

  • 使用 Prometheus 和 Grafana 實現監控
  • 用於錯誤跟蹤和性能的自動記錄
  • 針對模型漂移和更新的重新訓練管道

確保生產安全

  • 保護用於模型推理的 API
  • 身份驗證和授權機制
  • 解決數據隱私問題

案例研究和動手實驗

  • 部署情感分析模型
  • 擴展機器翻譯服務
  • 實現對圖像分類模型的監視

總結和後續步驟

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