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

# 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 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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ZenML vs Kubeflow: Elevate Your ML Workflows

Simplified Kubeflow Adoption

Enhanced Workflow Flexibility

Collaborative MLOps 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.