Compare ZenML vs
Valohai

Open source MLOps stack

ZenML offers a flexible, open-source alternative to Valohai for ML pipeline orchestration. Unlike Valohai's closed-source, all-in-one platform, ZenML seamlessly integrates with your existing infrastructure. Enjoy ZenML's intuitive Python-based SDK with decorators, while avoiding vendor lock-in. Accelerate your ML initiatives with ZenML's adaptable framework, allowing you to choose preferred tools for datasets, hyperparameter tuning, and distributed processing.
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ZenML
vs
Valohai

Leverage Existing Infrastructure

  • Seamlessly integrates with current orchestration infrastructure (Kubeflow, Kubernetes, AWS SageMaker)
  • No need to rebuild your entire ML stack
  • Enhances your existing setup with powerful MLOps capabilities
    • Dashboard mockup
    Dashboard mockup

    Intuitive Pipeline Development

  • Simple SDK-based interface with Python decorators
  • No complex YAML configurations required
  • @step and @pipeline decorators make ML workflows intuitive and efficient
  • Significantly reduces development time and errors

    • Freedom from Vendor Lock-in

  • Open-source and flexible, unlike Valohai's closed-source, end-to-end platform
  • Choose your preferred tools for datasets, hyperparameter tuning, and distributed processing
  • Maintain control over your ML infrastructure and workflows
    • Dashboard mockup

    ZenML has proven to be a critical asset in our machine learning toolbox, and we are excited to continue leveraging its capabilities to drive ADEO's machine learning initiatives to new heights

    François Serra
    ML Engineer / ML Ops / ML Solution architect at ADEO Services
    Feature-by-feature comparison

    Explore in Detail What Makes ZenML Unique

    Feature
    ZenML
    ZenML
    Valohai
    Valohai
    Infrastructure Integration Seamlessly integrates with existing orchestration infrastructure like Kubeflow, Kubernetes, AWS SageMaker Requires using Valohai's proprietary infrastructure
    Pipeline Development Intuitive Python-based SDK with @step and @pipeline decorators YAML-based configuration, which can be more complex
    Vendor Lock-in Open-source and flexible, allowing use of preferred tools for various ML tasks Closed-source with lock-in effect, requiring use of Valohai for all ML tasks
    Customization Highly customizable MLOps stack to fit specific needs Limited customization options within Valohai's ecosystem
    Learning Curve Gentle learning curve with familiar Python-based approach Steeper learning curve due to YAML configuration and platform-specific concepts
    End-to-End Platform Focuses on core MLOps functionalities, allowing integration with preferred tools Provides a complete end-to-end platform for ML workflows
    Scalability Scalable across various environments and infrastructures Scalable within Valohai's infrastructure
    Open Source Fully open-source, allowing community contributions and customizations Closed-source platform
    Code comparison
    ZenML and
    Valohai
    side by side
    ZenML
    ZenML
    from zenml import pipeline, step
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

@step
def ingest_data():
    return pd.read_csv("data/dataset.csv")

@step
def train_model(df):
    X, y = df.drop("target", axis=1), df["target"]
    model = RandomForestRegressor(n_estimators=100)
    model.fit(X, y)
    return model

@step
def evaluate_model(model, df):
    X, y = df.drop("target", axis=1), df["target"]
    rmse = mean_squared_error(y, model.predict(X)) ** 0.5
    print(f"RMSE: {rmse}")

@pipeline
def ml_pipeline():
    df = ingest_data()
    model = train_model(df)
    evaluate_model(model, df)

ml_pipeline()

    ‍

    Valohai
    Valohai
    
# valohai.yaml
---
- step:
    name: ingest_data
    image: python:3.9
    command:
      - python ingest_data.py
    outputs:
      - name: dataset
        path: dataset.csv

- step:
    name: train_model
    image: python:3.9
    command:
      - python train_model.py
    inputs:
      - name: dataset
        default: datum://ingest_data/dataset.csv
    outputs:
      - name: model
        path: model.pkl

- step:
    name: evaluate_model
    image: python:3.9
    command:
      - python evaluate_model.py
    inputs:
      - name: dataset
        default: datum://ingest_data/dataset.csv
      - name: model
        default: datum://train_model/model.pkl

# ingest_data.py
import pandas as pd

def ingest_data():
    df = pd.read_csv("data/dataset.csv")
    df.to_csv("/valohai/outputs/dataset.csv", index=False)

if __name__ == "__main__":
    ingest_data()

# train_model.py
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
import pickle

def train_model():
    df = pd.read_csv("/valohai/inputs/dataset.csv")
    X, y = df.drop("target", axis=1), df["target"]
    model = RandomForestRegressor(n_estimators=100)
    model.fit(X, y)
    with open("/valohai/outputs/model.pkl", "wb") as f:
        pickle.dump(model, f)

if __name__ == "__main__":
    train_model()

# evaluate_model.py
import pandas as pd
from sklearn.metrics import mean_squared_error
import pickle

def evaluate_model():
    df = pd.read_csv("/valohai/inputs/dataset.csv")
    with open("/valohai/inputs/model.pkl", "rb") as f:
        model = pickle.load(f)
    X, y = df.drop("target", axis=1), df["target"]
    rmse = mean_squared_error(y, model.predict(X)) ** 0.5
    print(f"RMSE: {rmse}")

if __name__ == "__main__":
    evaluate_model()

# To run the pipeline:
# valohai execution run --adhoc ingest_data
# valohai execution run --adhoc train_model
# valohai execution run --adhoc evaluate_model

    Flexibility and Customization

    ZenML's modular architecture allows for extensive customization and integration with your preferred tools and platforms, whereas Metaflow offers a more opinionated and rigid workflow structure.

    Focus on Usability and Adoption

    ZenML prioritizes simplicity and ease of use, providing comprehensive documentation, tutorials, and community support to facilitate faster adoption and productivity for teams of all skill levels.

    Agile ML Workflow Development

    ZenML's lightweight and flexible pipeline definition enables rapid iteration and experimentation, allowing data scientists and ML engineers to quickly prototype and refine ML workflows using a familiar Python-based syntax.

    Seamless Tool Integration

    ZenML seamlessly integrates with MLflow and other best-of-breed tools, allowing you to create a customized MLOps stack that fits your specific requirements. MLflow, as a standalone tool, may require more effort to integrate with other components in your ML ecosystem.

    Reproducibility and Governance

    With built-in support for data versioning and lineage tracking, ZenML ensures reproducibility, traceability, and governance of your ML pipelines, facilitating compliance and collaboration.

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    Expand Your Knowledge

    Broaden Your MLOps Understanding with ZenML

    AWS MLOps Made Easy: Integrating ZenML for Seamless Workflows

    Machine Learning Operations (MLOps) is crucial in today's tech landscape, even with the rise of Large Language Models (LLMs). Implementing MLOps on AWS, leveraging services like SageMaker, ECR, S3, EC2, and EKS, can enhance productivity and streamline workflows. ZenML, an open-source MLOps framework, simplifies the integration and management of these services, enabling seamless transitions between AWS components. MLOps pipelines consist of Orchestrators, Artifact Stores, Container Registry, Model Deployers, and Step Operators. AWS offers a suite of managed services, such as ECR, S3, and EC2, but careful planning and configuration are required for a cohesive MLOps workflow.

    Why deep learning development in production is (still) broken

    Software engineering best practices have not been brought into the machine learning space, with the side-effect that there is a great deal of technical debt in these code bases.

    How to Break Free from MLOps Orchestration Lock-in: A Technical Guide

    Unlock the potential of your ML infrastructure by breaking free from orchestration tool lock-in. This comprehensive guide explores proven strategies for building flexible MLOps architectures that adapt to your organization's evolving needs. Learn how to maintain operational efficiency while supporting multiple orchestrators, implement robust security measures, and create standardized pipeline definitions that work across different platforms. Perfect for ML engineers and architects looking to future-proof their MLOps infrastructure without sacrificing performance or compliance.

    Experience the ZenML Difference: Book Your Customized Demo

    Experience the ZenML Advantage

    • Seamlessly integrate with your existing ML infrastructure like Kubeflow and OpenShift AI
    • Develop pipelines effortlessly with intuitive Python decorators
    • Avoid vendor lock-in with our open-source, flexible framework
    • Customize your MLOps stack to fit your specific needs
    See ZenML's superior model orchestration in action
    Discover how ZenML offers more with your existing ML tools
    Find out why data security with ZenML outshines the rest
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