Effortlessly Expand Your ML Initiatives

Selecting the optimal MLOps framework is essential for achieving success in your machine learning endeavors. 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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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.

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.

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

Integration Flexibility	Limited integration with various tools and services	Custom integrations to support all possible workflows
Scalability	Scalable, but may require additional setup and configuration	Highly scalable for large datasets
Workflow Automation	Offers workflow automation through DAGs	Advanced automation capabilities
Data Versioning	Limited built-in data versioning capabilities	Comprehensive data versioning
Experiment Tracking	Limited native experiment tracking, often requires third-party tools	Detailed experiment tracking
Deployment Ease	Deployment can be complex, requiring setup and maintenance	Simplified deployment processes
Collaboration Tools	Basic collaboration support through shared repositories	Enhanced collaboration features
Customizability	Customizable, but requires coding and setup	Highly customizable to user needs
Pipeline Visualization	Offers DAG visualization for workflow overview	Rich pipeline visualization tools
Machine Learning Models	Supports various ML models, but may require additional integration; note ML-native	Support for a wide range of ML models
Monitoring and Logging	Provides logging and monitoring capabilities	Extensive monitoring and logging
Cost Efficiency	Open-source, but may incur costs for setup and maintenance	More cost-effective solutions
User Interface	Web-based UI for DAG management and monitoring	User-friendly and intuitive interface
Community and Support	Large and active community with extensive resources	Strong community support and resources
Continuous Integration/Deployment (CI/CD)	Can integrate with CI/CD tools, but requires setup	Robust CI/CD integration
Security Features	Offers role-based access control and other security features	Advanced security protocols

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