MLOps Infrastructure Design

from airflow import DAG # type: ignore
from airflow.operators.python_operator import PythonOperator # type: ignore
from airflow.providers.postgres.operators.postgres import PostgresOperator # type: ignore
from datetime import datetime

# Define custom functions for pipeline tasks
def train_model_function():
    print("Training the model...")

def deploy_model_function():
    print("Deploying the model...")

def monitor_model_function():
    print("Monitoring the model...")

# Define the DAG
with DAG(
    'ml_pipeline',
    start_date=datetime(2024, 1, 1),
    schedule_interval=None,
    catchup=False,
) as dag:

    # Data ingestion and transformation (using dbt)
    dbt_run = PostgresOperator(
        task_id='dbt_run',
        postgres_conn_id='postgres_default',
        sql='CALL dbt_run_model();',  # Replace with actual DBT run command
    )

    # Model training
    train_model = PythonOperator(
        task_id='train_model',
        python_callable=train_model_function,
    )

    # Model deployment
    deploy_model = PythonOperator(
        task_id='deploy_model',
        python_callable=deploy_model_function,
    )

    # Model monitoring
    monitor_model = PythonOperator(
        task_id='monitor_model',
        python_callable=monitor_model_function,
    )

    # Define task dependencies
    dbt_run >> train_model >> deploy_model >> monitor_model