- 熟悉機器學習工作流程和概念
- 對 Apache Airflow 有基本的瞭解，包括 DAG 和運算符
- 熟練掌握 Python 程式設計
觀眾
- 數據科學家
- 機器學習工程師
- AI 開發人員
Apache Airflow 是一個開源平臺，用於編排工作流和自動化複雜的數據管道。
這種講師指導的現場培訓（在線或現場）面向希望自動化和管理機器學習工作流程的中級參與者，包括使用 Apache Airflow 進行模型訓練、驗證和部署。
在本次培訓結束時，參與者將能夠：
- 設置 Apache Airflow 用於機器學習工作流編排。
- 自動執行數據預處理、模型訓練和驗證任務。
- 將 Airflow 與機器學習框架和工具整合。
- 使用自動化管道部署機器學習模型。
- 監控和優化生產中的機器學習工作流程。
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。
Machine Learning 的 Apache Airflow 簡介
- Apache Airflow 概述及其與數據科學的相關性
- 自動化機器學習工作流程的主要功能
- 為數據科學項目設置 Airflow
使用 Airflow 構建 Machine Learning 管道
- 為端到端ML工作流設計DAG
- 使用運算符進行數據攝取、預處理和特徵工程
- 計劃和管理管道依賴關係
模型訓練和驗證
- 使用 Airflow 自動執行模型訓練任務
- 將 Airflow 與 ML 框架集成（例如 TensorFlow、PyTorch）
- 驗證模型並存儲評估指標
模型部署和監控
- 使用自動化管道部署機器學習模型
- 使用 Airflow 任務監控已部署的模型
- 處理重新訓練和模型更新
高級定製和整合
- 為 ML 特定任務開發自訂運算元
- 將 Airflow 與雲平臺和 ML 服務整合
- 使用外掛程式和感測器擴展 Airflow 工作流
優化和擴展 ML 管道
- 提高大規模數據的工作流性能
- 使用 Celery 和 Kubernetes 擴展 Airflow 部署
- 生產級 ML 工作流的最佳實踐
案例研究和實際應用
- 使用 Airflow 實現 ML 自動化的真實範例
- 動手練習：構建端到端ML管道
- 討論 ML 工作流管理中的挑戰和解決方案
總結和後續步驟
United Arab Emirates - Apache Airflow for Data Science: Automating Machine Learning Pipelines
Qatar - Apache Airflow for Data Science: Automating Machine Learning Pipelines
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