Have you worked with Airflow and written DAGs?

Yes, I have worked with Apache Airflow and wrote DAGs (Directed Acyclic Graphs) for automating and orchestrating workflows in data processing and machine learning.

In Airflow, a DAG describes a sequence of tasks and their dependencies. Each task is an operator, such as PythonOperator for running Python functions, BashOperator for executing commands, etc.

Here's an example of a simple DAG that runs two tasks sequentially:

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

def task1():
    print('Task 1 completed')

def task2():
    print('Task 2 completed')

with DAG('example_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    t1 = PythonOperator(task_id='task1', python_callable=task1)
    t2 = PythonOperator(task_id='task2', python_callable=task2)

    t1 >> t2  # t2 depends on the successful completion of t1

Using Airflow allows scheduling, monitoring, and managing complex data pipelines, which is especially useful in ML projects for automating data preparation, model training, and deployment.