Scaling your Machine Learning Projects Seamlessly

Choosing the right MLOps platform is crucial for your machine learning success. 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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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.

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.

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

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 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 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",
            ),
        ]
    )