Compare ZenML vs
Apache Airflow

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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ZenML
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Apache Airflow

Begin locally without complex setup challenges

  • ZenML is accessible via a straightforward pip package that enables you to execute and monitor pipelines locally.
  • ZenML seamlessly integrates with your preferred orchestration layer, eliminating the need to learn different paradigms for development, staging, and production environments.
  • ZenML integrates with your chosen orchestration layer or can be extended to support your custom orchestration service.
Dashboard mockup
Dashboard mockup

Simplify infrastructure intricacies

  • Most orchestrators require some level of infrastructure knowledge for optimal utilization - ZenML abstracts away that complexity.
  • ZenML decouples infrastructure setup, such as Docker building, from the application logic and automates the tedious aspects.
  • ZenML prioritizes the seamless collaboration between MLOps Engineers, ML Engineers, and Data Scientists.

Effortlessly transition between orchestrators based on your context

  • You can effortlessly switch between different orchestration services with a single click - from development to staging to production environments.
  • The more engineering-oriented team members maintain control over the productionalization process due to the framework's extensibility.
  • ZenML manages the challenge of packaging your code into Docker for deployment to your preferred orchestration service.
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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
ZenML
Apache Airflow
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

# 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
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

Streamlined ML Workflow Initialization

ZenML guarantees swifter initialization, surpassing orchestrators for prompt, optimized ML workflows.

Supporting All Your Tools

ZenML is a native interface to the whole end-to-end machine learning lifecycle, taking you beyond just orchestration.

Unrivaled User Assistance

ZenML excels with dedicated support, offering personalized assistance beyond standard orchestrators.

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Expand Your Knowledge

Broaden Your MLOps Understanding with ZenML

Deploy your ML models with KServe and ZenML

How to use ZenML and KServe to deploy serverless ML models in just a few steps.

Easy MLOps pipelines: 1-click deployments for AWS, GCP, and Azure

Streamline your machine learning platform with ZenML. Learn how ZenML's 1-click cloud stack deployments simplify setting up MLOps pipelines on AWS, GCP, and Azure.

From Chaos to Control: A Guide to Scaling MLOps Automation

Discover how organizations can transform their machine learning operations from manual, time-consuming processes into streamlined, automated workflows. This comprehensive guide explores common challenges in scaling MLOps, including infrastructure management, model deployment, and monitoring across different modalities. Learn practical strategies for implementing reproducible workflows, infrastructure abstraction, and comprehensive observability while maintaining security and compliance. Whether you're dealing with growing pains in ML operations or planning for future scale, this article provides actionable insights for building a robust, future-proof MLOps foundation.

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