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.
ZenML
vs
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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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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