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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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.

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.

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()

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.

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Expand Your Knowledge

Broaden Your MLOps Understanding with ZenML

Banking on AI: Implementing Compliant MLOps for Financial Institutions

Traditional banks face growing pressure to deploy machine learning rapidly while meeting strict regulatory requirements. This blog post explores how modern MLOps practices, like automated data lineage, validation testing, and model observability can help financial institutions bridge the gap. Featuring real-world insights from NatWest and an open-source ZenML pipeline, it offers a practical roadmap for compliant, scalable AI deployment.

Understanding the AI Act: February 2025 Updates and Implications

The EU AI Act, now partially in effect as of February 2025, introduces comprehensive regulations for artificial intelligence systems with significant implications for global AI development. This landmark legislation categorizes AI systems based on risk levels - from prohibited applications to high-risk and limited-risk systems - establishing strict requirements for transparency, accountability, and compliance. The Act imposes substantial penalties for violations, up to €35 million or 7% of global turnover, and provides a clear timeline for implementation through 2027. Organizations must take immediate action to audit their AI systems, implement robust governance infrastructure, and enhance development practices to ensure compliance, with tools like ZenML offering technical solutions for meeting these regulatory requirements.

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.

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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

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