Introduction to Apache Airflow
Apache Airflow is a platform for orchestrating complex workflows β automating and scheduling interdependent tasks in data and machine learning pipelines.
Airflow organizes workflows as Directed Acyclic Graphs (DAGs), where each node represents a task and the edges define dependencies between them. This ensures each task runs in the correct order β for instance, a model training step can only start after data preprocessing completes.
Airflow's scheduler automatically executes these tasks according to a defined schedule, ensuring consistency and reproducibility. Engineers can easily rerun failed tasks, monitor progress through the Airflow UI, and scale workflows as projects grow.
Airflow enables reproducible, automated workflows for data and ML tasks. Explore the official Airflow documentation and community examples to deepen your understanding of workflow orchestration in production environments.
Basic DAG Example
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
def print_hello():
print("Hello from Airflow DAG!")
default_args = {
"owner": "mlops_engineer",
"retries": 1,
"retry_delay": timedelta(minutes=5),
}
dag = DAG(
"hello_airflow_example",
default_args=default_args,
description="A simple DAG example",
schedule_interval=timedelta(days=1),
start_date=datetime(2024, 6, 1),
catchup=False,
)
hello_task = PythonOperator(
task_id="say_hello",
python_callable=print_hello,
dag=dag,
)
Airflow is the backbone of workflow orchestration in MLOps. It allows you to automate retraining, data ingestion, and evaluation β all defined as Python code and executed in order.
Check out the official Airflow documentation for examples of production DAGs and tips on scaling Airflow deployments.
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Apache Airflow is a platform for orchestrating complex workflows β automating and scheduling interdependent tasks in data and machine learning pipelines.
Airflow organizes workflows as Directed Acyclic Graphs (DAGs), where each node represents a task and the edges define dependencies between them. This ensures each task runs in the correct order β for instance, a model training step can only start after data preprocessing completes.
Airflow's scheduler automatically executes these tasks according to a defined schedule, ensuring consistency and reproducibility. Engineers can easily rerun failed tasks, monitor progress through the Airflow UI, and scale workflows as projects grow.
Airflow enables reproducible, automated workflows for data and ML tasks. Explore the official Airflow documentation and community examples to deepen your understanding of workflow orchestration in production environments.
Basic DAG Example
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
def print_hello():
print("Hello from Airflow DAG!")
default_args = {
"owner": "mlops_engineer",
"retries": 1,
"retry_delay": timedelta(minutes=5),
}
dag = DAG(
"hello_airflow_example",
default_args=default_args,
description="A simple DAG example",
schedule_interval=timedelta(days=1),
start_date=datetime(2024, 6, 1),
catchup=False,
)
hello_task = PythonOperator(
task_id="say_hello",
python_callable=print_hello,
dag=dag,
)
Airflow is the backbone of workflow orchestration in MLOps. It allows you to automate retraining, data ingestion, and evaluation β all defined as Python code and executed in order.
Check out the official Airflow documentation for examples of production DAGs and tips on scaling Airflow deployments.
Thanks for your feedback!
