Compare ZenML vs
Dagster

Orchestrate Your Data Pipelines with Ease

Discover how ZenML stacks up against Dagster in the world of data pipeline orchestration. While Dagster offers a flexible, open-source platform for building and managing data pipelines, ZenML provides a more specialized solution focused on machine learning workflows. Compare ZenML's ML-centric features and integrations with Dagster's general-purpose pipeline orchestration capabilities. Learn how ZenML can streamline your ML operations with its intuitive pipeline definition, built-in experiment tracking, and seamless integration with popular ML frameworks, while Dagster caters to a broader range of data engineering and ETL use cases.
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ZenML
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Dagster

ML-Centric Pipeline Orchestration

  • ZenML is designed specifically for machine learning pipelines, offering a more specialized and optimized experience for ML workflows.
  • Leverage ZenML's built-in integrations with popular ML frameworks and libraries, such as scikit-learn, TensorFlow, and PyTorch.
  • Benefit from ZenML's ML-specific features, like experiment tracking, model registry, and hyperparameter tuning.
    • Dashboard mockup
    Dashboard mockup

    Intuitive Pipeline Definition

  • ZenML provides a clean and intuitive way to define ML pipelines using Python decorators and a familiar syntax.
  • Focus on the core logic of your ML workflows without getting bogged down in complex pipeline configuration.
  • Easily parameterize and reuse pipeline components across different experiments and projects.

    • Seamless MLOps Integration

    • ZenML seamlessly integrates with your existing MLOps stack, including version control systems, CI/CD pipelines, and model serving platforms.
    • Leverage ZenML's built-in support for popular cloud platforms, such as AWS, Google Cloud, and Microsoft Azure, for easy deployment and scaling.
    • Benefit from ZenML's strong focus on reproducibility, lineage tracking, and governance, ensuring your ML workflows are reliable and compliant.
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    Feature-by-feature comparison

    Explore in Detail What Makes ZenML Unique

    Feature
    ZenML
    ZenML
    Dagster
    Dagster
    ML Workflow Orchestration Specialized for machine learning pipelines General-purpose data pipeline orchestration not purpose-built for MLOps
    ML Framework Integration Built-in integrations with popular ML frameworks (scikit-learn, TensorFlow, PyTorch) Requires custom integration with ML frameworks
    Experiment Tracking Built-in experiment tracking and comparison Relies on external tools for experiment tracking
    Model Registry Integrated model registry for versioning and deployment No built-in model registry
    Data Processing Supports data processing tasks within ML pipelines Robust support for data processing and ETL workflows
    Pipeline Definition Clean and intuitive pipeline definition using Python decorators Flexible pipeline definition using Python or YAML
    Cloud Integration Built-in support for popular cloud platforms (AWS, GCP, Azure) Integrates with various cloud platforms and data stores
    Scalability Scales ML workloads across different compute backends Scales data pipelines through various execution engines
    Workflow Scheduling Supports scheduled execution of ML pipelines Robust scheduling and triggering of data pipelines
    Community and Ecosystem Growing community focused on ML workflows Large and active community around data engineering and ETL
    Code comparison
    ZenML and
    Dagster
    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()
    Dagster
    Dagster
    from dagster import pipeline, solid
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

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

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

@solid
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.execute_in_process()

    ML-Centric Design

    ZenML is purpose-built for machine learning workflows, providing a more specialized and optimized experience compared to Dagster's general-purpose pipeline orchestration.

    Seamless Integration with ML Frameworks

    ZenML offers built-in integrations with popular ML frameworks, such as scikit-learn, TensorFlow, and PyTorch, making it easier to incorporate ML tasks into your pipelines.

    Built-in Experiment Tracking and Model Registry

    With ZenML, you can leverage built-in experiment tracking and model registry capabilities, streamlining the management of your ML experiments and model versions.

    Intuitive Pipeline Definition

    ZenML provides a clean and intuitive way to define ML pipelines using Python decorators, allowing you to focus on the core logic of your workflows.

    Strong Focus on MLOps and Reproducibility

    ZenML places a strong emphasis on MLOps best practices, ensuring your ML workflows are reproducible, traceable, and compliant with governance requirements.

    Outperform Orchestrators: Book Your Free ZenML Strategy Talk

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    Expand Your Knowledge

    Broaden Your MLOps Understanding with ZenML

    Predicting the winner of a DotA 2 match using distributed deep learning pipelines

    ZenML makes it easy to setup training pipelines that give you all the benefits of cached steps.

    Boost Your MLOps Efficiency: Integrate ZenML and Comet for Better Experiment Tracking

    This blog post discusses the integration of ZenML and Comet, an open-source machine learning pipeline management platform, to enhance the experimentation process. ZenML is an extensible framework for creating portable, production-ready pipelines, while Comet is a platform for tracking, comparing, explaining, and optimizing experiments and models. The combination offers seamless experiment tracking, enhanced visibility, simplified workflow, improved collaboration, and flexible configuration. The process involves installing ZenML and enabling Comet integration, registering the Comet experiment tracker in the ZenML stack, and customizing experiment settings.

    The Ultimate Guide to LLM Batch Inference with OpenAI and ZenML

    OpenAI's Batch API allows you to submit queries for 50% of what you'd normally pay. Not all their models work with the service, but in many use cases this will save you lots of money on your LLM inference, just so long as you're not building a chatbot!

    Experience the ZenML Difference: Book Your Customized Demo

    Streamline Your ML Workflows with ZenML

    • Discover how ZenML's ML-centric design can simplify and optimize your machine learning pipelines
    • Leverage built-in integrations with popular ML frameworks and benefit from experiment tracking and model registry capabilities
    • Experience the power of intuitive pipeline definition and strong MLOps principles with ZenML
    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
    Use Open Source
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