ZenML vs Kubeflow: Elevate Your ML Workflows

ZenML is a lightweight alternative to Kubeflow, the Kubernetes-native platform for machine learning. While Kubeflow offers robust orchestration capabilities for ML workflows on Kubernetes, ZenML provides a more flexible and user-friendly approach to building, deploying, and managing ML pipelines at scale. ZenML's intuitive workflow management simplifies MLOps across various environments, not just Kubernetes. Leverage ZenML's adaptability and ease of use to accelerate your ML initiatives and drive innovation across your organization, without the steep learning curve and infrastructure demands of Kubeflow.

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Simplified Kubeflow Adoption

ZenML provides a user-friendly abstraction layer on top of Kubeflow, making it easier to adopt and utilize its powerful features.
With ZenML, you can quickly set up and manage Kubeflow pipelines without deep Kubernetes expertise, reducing the learning curve.
ZenML's intuitive interface and pre-built integrations streamline the process of deploying and monitoring ML workflows on Kubeflow.

Enhanced Workflow Flexibility

ZenML allows you to seamlessly integrate Kubeflow with other best-of-breed tools in your ML ecosystem, providing greater flexibility.
With ZenML, you can easily extend and customize your Kubeflow pipelines to incorporate additional data sources, models, and deployment targets.
ZenML's modular architecture enables you to mix and match components, ensuring your Kubeflow workflows align with your unique requirements.

Collaborative MLOps at Scale

ZenML fosters collaboration among data scientists, ML engineers, and DevOps teams, facilitating seamless handoffs and reducing silos.
With ZenML's version control and governance features, you can manage Kubeflow pipelines across multiple environments and ensure reproducibility.
ZenML's centralized dashboard provides visibility into the entire MLOps lifecycle, enabling teams to monitor and optimize Kubeflow workflows at scale.

After a benchmark on several solutions, we choose ZenML for its stack flexibility and its incremental process. We started from small local pipelines and gradually created more complex production ones. It was very easy to adopt.

Clément Depraz

Data Scientist at Brevo

Feature-by-feature comparison

Explore in Detail What Makes ZenML Unique

Feature

ZenML

Kubeflow

Workflow Orchestration	Provides a flexible and user-friendly orchestration layer on top of Kubeflow	Offers powerful Kubernetes-native orchestration for ML workflows
Ease of Use	Simplifies the adoption and management of Kubeflow pipelines with an intuitive interface	Requires Kubernetes expertise to effectively utilize its features
Integration Flexibility	Seamlessly integrates Kubeflow with other MLOps tools for a customized stack	Primarily focuses on Kubernetes-based integrations and extensions
Switch your orchestrator, keep your code	Keep the same code when switching orchestrator	Requires significant rewriting to use Kubeflow code with a different orchestrator
Pipeline Customization	Enables easy customization and extension of Kubeflow pipelines	Allows customization but may require more Kubernetes knowledge
Collaborative MLOps	Facilitates collaboration among teams with version control and governance features	Provides collaboration features but may require additional setup
Scalability	Leverages Kubeflow's scalability while providing an abstraction layer for ease of use	Highly scalable for large-scale ML workflows on Kubernetes
Experiment Tracking	Integrates with MLflow and other tools for comprehensive experiment tracking	Offers Kubeflow Metadata for experiment tracking and artifact management
Model Deployment	Simplifies the deployment of models using Kubeflow with pre-built integrations	Supports various deployment options, including Kubeflow Serving
Monitoring and Logging	Provides centralized monitoring and logging for Kubeflow pipelines	Offers Kubeflow Metadata for logging and monitoring
Community and Support	Growing community with active support and resources	Large and active community with extensive resources and support
MLOps Lifecycle Coverage	Covers the entire MLOps lifecycle, from data preparation to model monitoring	Focuses primarily on orchestration, deployment, and serving
Learning Curve	Reduces the learning curve for adopting Kubeflow with a user-friendly abstraction layer	Requires Kubernetes expertise to effectively utilize its full set of features
Hybrid and Multi-Cloud	Supports hybrid and multi-cloud deployments with Kubeflow integration	Enables hybrid and multi-cloud deployments on Kubernetes
GPU and Distributed Computing	Seamlessly leverages Kubeflow's GPU and distributed computing capabilities	Provides strong support for GPU and distributed computing workloads

Code comparison

ZenML and

Kubeflow

side by side

ZenML


# zenml integration install kubeflow
# zenml orchestrator register kf_orchestrator -f kubeflow ...
# zenml stack update my_stack -o kf_orchestrator

from zenml import pipeline, step

@step
def preprocess_data(data_path: str) -> str:
    # Preprocessing logic here
    return processed_data

@step
def train_model(data: str):
    # Model training logic here
    return model

@pipeline
def my_pipeline(data_path: str):
    processed_data = preprocess_data(data_path)
    model = train_model(processed_data)

# Run the pipeline
my_pipeline(data_path="path/to/data")

Kubeflow


# Kubeflow Pipelines SDK
import kfp
from kfp import dsl

def preprocess_op(data_path):
    return dsl.ContainerOp(
        name='Preprocess Data',
        image='preprocess-image:latest',
        arguments=['--data_path', data_path]
    )

def train_op(data):
    return dsl.ContainerOp(
        name='Train Model',
        image='train-image:latest',
        arguments=['--data', data]
    )

@dsl.pipeline(
    name='My ML Pipeline',
    description='A sample ML pipeline'
)
def my_pipeline(data_path: str):
    preprocess_task = preprocess_op(data_path)
    train_task = train_op(preprocess_task.output)

# Compile and run the pipeline
kfp.compiler.Compiler().compile(my_pipeline, 'pipeline.yaml')
client = kfp.Client()
client.create_run_from_pipeline_func(my_pipeline, arguments={})