Seamlessly integrate the robustness of Apache Airflow with the ML-centric capabilities of ZenML pipelines. This powerful combination simplifies the orchestration of complex machine learning workflows, enabling data scientists and engineers to focus on building high-quality models while leveraging Airflow's proven production-grade features.
from zenml import step, pipeline
from zenml.integrations.airflow.flavors.airflow_orchestrator_flavor import AirflowOrchestratorSettings
@step
def my_step():
print("Running in Airflow!")
airflow_settings = AirflowOrchestratorSettings(
operator="airflow.providers.docker.operators.docker.DockerOperator",
operator_args={}
)
@pipeline(settings={"orchestrator.airflow": airflow_settings})
def my_airflow_pipeline():
my_step()
if __name__ == "__main__":
my_airflow_pipeline()]
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