Compare ZenML vs
Kubeflow

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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ZenML
vs
Kubeflow

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.
Dashboard mockup
Dashboard mockup

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.
Dashboard mockup

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
ZenML
Kubeflow
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

# 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

# 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={})

Streamlined ML Workflow Initialization

ZenML guarantees swifter initialization, surpassing orchestrators for prompt, optimized ML workflows.

Supporting All Your Tools

ZenML is a native interface to the whole end-to-end machine learning lifecycle, taking you beyond just orchestration.

Unrivaled User Assistance

ZenML excels with dedicated support, offering personalized assistance beyond standard orchestrators.

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Expand Your Knowledge

Broaden Your MLOps Understanding with ZenML

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The Framework Way is the Best Way: the pitfalls of MLOps and how to avoid them

As our AI/ML projects evolve and mature, our processes and tooling also need to keep up with the growing demand for automation, quality and performance. But how can we possibly reconcile our need for flexibility with the overwhelming complexity of a continuously evolving ecosystem of tools and technologies? MLOps frameworks promise to deliver the ideal balance between flexibility, usability and maintainability, but not all MLOps frameworks are created equal. In this post, I take a critical look at what makes an MLOps framework worth using and what you should expect from one.

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