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建立時間： 2026-05-30 來源： https://x.com/devopscube/status/2052249151405007119
Summary
DevOpsCube promotes their guide on running Apache Airflow on Kubernetes, covering DAGs, executors, GitSync for DAG management, and Day 2 operational insights. Airflow 3 is a redesign targeting AI/ML and near-real-time workloads. The post positions Airflow knowledge as a transferable foundation for Kubeflow and similar platforms.
DevOpsCube 介紹在 Kubernetes 上運行 Apache Airflow 的完整指南，涵蓋 DAG、executor、GitSync 等主題。Airflow 3 針對 AI/ML 工作負載進行了全面重新設計，80,000 個組織使用它，其中 30% 用於 MLOps 工作流程。
Key Points
- Airflow: open-source workflow orchestration via DAGs
- DAG concepts transfer directly to Kubeflow
- Airflow 3: major redesign for AI/ML and near-real-time workloads
- GitSync enables DAG version control via git
- 80k orgs use it; 30% for MLOps, 10% for GenAI workflows
Insights
The post is primarily promotional but the Airflow → Kubeflow conceptual bridge is genuinely useful for ML practitioners entering MLOps. Understanding DAG-based orchestration once transfers to most pipeline tools.
Connections
Raw Excerpt
“Learning Airflow makes it much easier to understand platforms like Kubeflow because the core concepts are very similar: DAGs, task orchestration, pipeline execution.”