Compare ZenML vs
Kedro

ZenML vs Kedro: Scaling your Machine Learning Projects Seamlessly

In this guide, we compare ZenML with Kedro to highlight which framework best fits your needs for scalability, ease of use, and robust feature sets. Discover the key differences that will streamline your ML operations and propel your projects forward.
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ZenML
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Kedro

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.
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Dashboard mockup

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.

Switch between orchestrators depending 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.
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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
Kedro
Kedro
Integration Flexibility Custom integrations for diverse workflows Limited to specific platforms
Workflow Automation Advanced automation capabilities Basic automation features
Experiment Tracking Detailed experiment tracking Limited tracking capabilities
Collaboration Tools Enhanced collaboration features Basic collaboration support
Pipeline Visualization Rich pipeline visualization tools Basic or limited visualization
Monitoring and Logging Extensive monitoring and logging Basic monitoring and logging
Cost Efficiency More cost-effective solutions Potentially higher operational costs
User Interface User-friendly and intuitive interface Varies, often less user-friendly
Community and Support Strong community support and resources Limited community resources
Continuous Integration/Deployment (CI/CD) Robust CI/CD integration Often lacks full CI/CD integration
Security Features Advanced security protocols Basic security features
Code comparison
ZenML and
Kedro
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
Kedro
Kedro

# Kedro pipeline syntax
from kedro.pipeline import Pipeline, node

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

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

def create_pipeline(**kwargs):
    return Pipeline(
        [
            node(
                func=data_preprocessing,
                inputs="raw_data",
                outputs="preprocessed_data",
                name="preprocess_data",
            ),
            node(
                func=model_training,
                inputs="preprocessed_data",
                outputs="trained_model",
                name="train_model",
            ),
        ]
    )

Rapid ML Workflow Setup

ZenML ensures a faster setup, outpacing orchestrators for quick, efficient ML workflows.

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Expand Your Knowledge

Broaden Your MLOps Understanding with ZenML

Boost Your MLOps Efficiency: Integrate ZenML and Comet for Better Experiment Tracking

This blog post discusses the integration of ZenML and Comet, an open-source machine learning pipeline management platform, to enhance the experimentation process. ZenML is an extensible framework for creating portable, production-ready pipelines, while Comet is a platform for tracking, comparing, explaining, and optimizing experiments and models. The combination offers seamless experiment tracking, enhanced visibility, simplified workflow, improved collaboration, and flexible configuration. The process involves installing ZenML and enabling Comet integration, registering the Comet experiment tracker in the ZenML stack, and customizing experiment settings.

Need an open-source data annotation tool? We've got you covered!

We put together a list of 48 open-source annotation and labeling tools to support different kinds of machine-learning projects.

Elevate Your Cloud MLOps with ZenML

Why use ZenML alongside AWS / GCP / Azure MLOps platforms? Let's dive into why ZenML complements and enhance existing cloud MLOps infrastructure.

Experience the ZenML Difference: Book Your Customized Demo

See ZenML's superior model orchestration in action
Discover how ZenML offers more with your existing ML tools
Find out why data security with ZenML outshines the rest
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