Argo Workflows vs Prefect vs Airflow — Best for ML Pipelines 2026

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  name: ml-training-pipeline
spec:
  entrypoint: train-model
  templates:
    - name: train-model
      dag:
        tasks:
          - name: data-ingestion
            template: ingest
          - name: feature-engineering
            template: features
            dependencies: [data-ingestion]
          - name: train
            template: train
            dependencies: [feature-engineering]
          - name: evaluate
            template: evaluate
            dependencies: [train]
          - name: deploy
            template: deploy
            dependencies: [evaluate]
            when: "{{tasks.evaluate.outputs.parameters.accuracy}} > 0.85"
 
    - name: ingest
      container:
        image: my-ml-pipeline:latest
        command: [python, ingest.py]
        resources:
          requests: {cpu: 100m, memory: 512Mi}
        env:
          - name: S3_BUCKET
            value: my-data-bucket
 
    - name: train
      container:
        image: my-ml-pipeline:latest
        command: [python, train.py]
        resources:
          requests: {cpu: 4, memory: 16Gi}
          limits: {nvidia.com/gpu: "1"}   # GPU for training

from prefect import flow, task
from prefect.deployments import Deployment
 
@task(retries=2, retry_delay_seconds=30)
def ingest_data(bucket: str) -> str:
    """Download and validate training data."""
    import boto3
    s3 = boto3.client("s3")
    s3.download_file(bucket, "data/train.parquet", "/tmp/train.parquet")
    return "/tmp/train.parquet"
 
@task
def engineer_features(data_path: str) -> str:
    import pandas as pd
    df = pd.read_parquet(data_path)
    # ... feature engineering
    df.to_parquet("/tmp/features.parquet")
    return "/tmp/features.parquet"
 
@task(tags=["gpu"])
def train_model(features_path: str) -> dict:
    # ... training logic
    return {"accuracy": 0.91, "model_path": "/tmp/model.pkl"}
 
@task
def evaluate_model(model_info: dict) -> bool:
    return model_info["accuracy"] > 0.85
 
@task
def deploy_model(model_info: dict):
    # ... deployment logic
    pass
 
@flow(name="ml-training-pipeline")
def train_pipeline(bucket: str = "my-data-bucket"):
    data_path = ingest_data(bucket)
    features_path = engineer_features(data_path)
    model_info = train_model(features_path)
    
    if evaluate_model(model_info):
        deploy_model(model_info)
    else:
        raise ValueError(f"Model accuracy {model_info['accuracy']} below threshold")
 
# Run locally
if __name__ == "__main__":
    train_pipeline()
 
# Or deploy to Prefect Cloud / self-hosted
deployment = Deployment.build_from_flow(
    flow=train_pipeline,
    name="production",
    work_queue_name="kubernetes",
    schedule={"cron": "0 2 * * *"}  # Nightly at 2 AM
)
deployment.apply()

from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.amazon.aws.operators.sagemaker import SageMakerTrainingOperator
from datetime import datetime, timedelta
 
def ingest_data(**context):
    # ... ingestion logic
    context['task_instance'].xcom_push(key='data_path', value='/tmp/train.parquet')
 
def engineer_features(**context):
    data_path = context['task_instance'].xcom_pull(key='data_path', task_ids='ingest')
    # ... feature engineering
 
with DAG(
    "ml_training_pipeline",
    schedule_interval="0 2 * * *",
    start_date=datetime(2026, 1, 1),
    catchup=False,
    default_args={
        "retries": 2,
        "retry_delay": timedelta(minutes=5),
    }
) as dag:
    
    ingest = PythonOperator(
        task_id="ingest",
        python_callable=ingest_data,
    )
    
    features = PythonOperator(
        task_id="features",
        python_callable=engineer_features,
    )
    
    # Use SageMaker for GPU training
    train = SageMakerTrainingOperator(
        task_id="train",
        config={"TrainingJobName": "my-training-job-{{ ds_nodash }}"},
    )
    
    ingest >> features >> train