eladkal commented on code in PR #70547: URL: https://github.com/apache/airflow/pull/70547#discussion_r3680948331
########## README.md: ########## @@ -84,6 +84,8 @@ Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow sched Airflow works best with workflows that are mostly static and slowly changing. When the Dag structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include [Luigi](https://github.com/spotify/luigi), [Oozie](https://oozie.apache.org/) and [Azkaban](https://azkaban.github.io/). +Beyond traditional data pipelines, Airflow is widely used to orchestrate machine learning workflows — training, retraining, evaluation, and deployment — and increasingly to orchestrate agentic and LLM-based workloads, coordinating the steps of an AI pipeline (data prep, tool calls, model invocation, evaluation) rather than acting as the agent itself. This isn't a new direction: teams have run ML and AI workloads on Airflow for years, and the ecosystem of providers supporting these use cases (see the [AI & ML section of the Airflow registry](https://airflow.apache.org/registry/explore/)) continues to grow. Review Comment: ```suggestion Beyond traditional data pipelines, Airflow is widely used to orchestrate machine learning workflows — training, retraining, evaluation, and deployment — and increasingly to orchestrate agentic and LLM-based workloads, coordinating the steps of an AI pipeline (data prep, tool calls, model invocation, evaluation) rather than acting as the agent itself. This isn't a new direction: teams have run ML and AI workloads on Airflow for years, and the ecosystem of providers supporting these use cases (see the [AI & ML section of the Airflow registry](https://airflow.apache.org/registry/providers/?category=ai-ml)) continues to grow. ``` ########## README.md: ########## @@ -84,6 +84,8 @@ Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow sched Airflow works best with workflows that are mostly static and slowly changing. When the Dag structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include [Luigi](https://github.com/spotify/luigi), [Oozie](https://oozie.apache.org/) and [Azkaban](https://azkaban.github.io/). +Beyond traditional data pipelines, Airflow is widely used to orchestrate machine learning workflows — training, retraining, evaluation, and deployment — and increasingly to orchestrate agentic and LLM-based workloads, coordinating the steps of an AI pipeline (data prep, tool calls, model invocation, evaluation) rather than acting as the agent itself. This isn't a new direction: teams have run ML and AI workloads on Airflow for years, and the ecosystem of providers supporting these use cases (see the [AI & ML section of the Airflow registry](https://airflow.apache.org/registry/explore/)) continues to grow. Review Comment: updated -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
