本課程針對評估生成式和預測式 AI 模型,向機器學習從業人員介紹相關的基礎工具、技術和最佳做法。模型評估是機器學習的重要領域,確保這類系統能在正式環境中提供可靠、準確且成效優異的結果。
學員將深入瞭解多種評估指標與方法,以及適用於不同模型類型和工作的應用方式。此外,也會特別介紹生成式 AI 模型帶來的獨特難題,並提供有效的應對策略。透過 Google Cloud Vertex AI 平台,學員將瞭解在模型挑選、最佳化和持續監控方面,該如何導入穩健的評估程序。
瞭解預測式 AI 與生成式 AI 模型評估作業之間的細微差異,以及評估作業在 MLOps 生命週期的重要性。
為不同生成式 AI 工作找出並套用合適的評估指標。
利用豐富的 Vertex AI 評估服務,有效衡量生成式 AI,包括以運算資源和模型為基礎的方法。
導入最佳做法來評估大型語言模型,確保部署於正式環境的模型穩固可靠。
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本課程針對評估生成式和預測式 AI 模型,向機器學習從業人員介紹相關的基礎工具、技術和最佳做法。模型評估是機器學習的重要領域,確保這類系統能在正式環境中提供可靠、準確且成效優異的結果。
學員將深入瞭解多種評估指標與方法,以及適用於不同模型類型和工作的應用方式。此外,也會特別介紹生成式 AI 模型帶來的獨特難題,並提供有效的應對策略。透過 Google Cloud Vertex AI 平台,學員將瞭解在模型挑選、最佳化和持續監控方面,該如何導入穩健的評估程序。
本單元介紹課程目標,並概要說明課程結構。
本單元介紹評估模型時會遇到的難題,以及 Vertex AI 提供的解決方案。
3 videos1 reading1 assignment
本單元說明評估生成式 AI 工作時會出現的難題,以及克服這些難題的最佳做法。此外,也會介紹 Vertex AI 提供的多種模型評估服務,以及 Vertex AI 自動化指標、自動並排比較和安全偏誤評估服務。
7 videos1 reading1 assignment
本單元介紹最重要的概念、工具、技術和產品,概要說明整個課程內容。
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