Compare ZenML vs

ZenML vs Airflow: Effortlessly Expand Your ML Initiatives

This article contrasts ZenML and Airflow to emphasize which platform aligns best with your requirements for scalability, user-friendliness, and comprehensive functionality. Uncover the primary distinctions that will optimize your ML workflows and accelerate your project progress.

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Start locally without complicated setup hassle

ZenML is available as a simple pip package that lets you run and track pipelines locally.
ZenML integrates with your orchestration layer of choice, avoiding having to learn different paradigms for dev, staging, and prod.
ZenML integrates with your orchestration layer of choice or can be extended with your own orchestration service.

Dashboard mockup showing local-to-production workflow

Abstract away infrastructure complexity

Most orchestrators assume some form of infrastructure knowledge to use them maximally — ZenML abstracts that complexity away.
ZenML separates infrastructure setup like Docker building from the application logic, and automates the tedious parts.
ZenML focuses on the handovers between MLOps Engineers, ML Engineers, and Data Scientists.

Dashboard mockup showing collaboration features

Switch between orchestrators depending on your context

You can switch between different orchestration services with a single click — from dev to staging to production.
The more engineering-minded in the team still retain control over their productionalization because the framework is extensible.
ZenML handles the pain of packaging your code into Docker to be deployed to your orchestration service of choice.

Dashboard mockup showing productionalization workflow

“ZenML allows orchestrating ML pipelines independent of any infrastructure or tooling choices. ML teams can free their minds of tooling FOMO from the fast-moving MLOps space, with the simple and extensible ZenML interface. No more vendor lock-in, or massive switching costs!”

Richard Socher

Former Chief Scientist Salesforce and Founder of You.com

Feature-by-feature comparison

Explore in Detail What Makes ZenML Unique

Feature

ZenML

Apache Airflow

ML Experiment Tracking	Built-in experiment tracking tailored for ML workflows	Limited native experiment tracking, often requires third-party tools
Data Versioning	Native data versioning for reproducibility and lineage	Limited built-in data versioning capabilities
ML Deployment	Streamlined deployment of ML models to production	Deployment of ML models can be complex, requiring additional setup
Integration Flexibility	Seamless integration with ML frameworks and tools out-of-the-box	Flexible but may require more setup for ML-specific tools
ML-Centric Design	Purpose-built for machine learning projects end-to-end	General purpose workflow orchestration, not ML-native
Scalability	Designed to scale ML workloads with minimal overhead	Scalable, but may require additional setup and configuration
Collaboration	Collaborative features tailored for ML teams	Basic collaboration through shared repositories and workflows

Code comparison

ZenML and Apache Airflow side by side

ZenML

# ZenML pipeline syntax
from zenml import pipeline, step

@step
def data_preprocessing(data):
    ... # preprocessing logic

@step
def model_training(preprocessed_data):
    ... # model training logic

@pipeline
def ml_pipeline(data):
    preprocessed_data = data_preprocessing(data)
    trained_model = model_training(preprocessed_data)
    return trained_model

Apache Airflow

# Airflow DAG syntax
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2023, 1, 1),
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

def data_preprocessing(**kwargs):
    ... # preprocessing logic

def model_training(**kwargs):
    ... # model training logic

with DAG('ml_pipeline', default_args=default_args, schedule_interval=timedelta(days=1)) as dag:
    preprocess_task = PythonOperator(
        task_id='preprocess_data',
        python_callable=data_preprocessing,
    )
    
    train_model_task = PythonOperator(
        task_id='train_model',
        python_callable=model_training,
    )
    
    preprocess_task >> train_model_task