ZenML vs ClearML - Streamline Your ML Workflows

Streamline Your ML Workflows

ZenML offers a flexible, integration-rich alternative to ClearML for ML pipeline orchestration. Unlike ClearML's rigid, all-in-one approach, ZenML provides an adaptable MLOps framework that integrates seamlessly with various tools. Experience accelerated ML initiatives with ZenML's flexible architecture, collaborative features, and simplified setup, requiring less infrastructure knowledge to get started.

Comprehensive MLOps Solution

ZenML provides a highly adaptable MLOps solution, allowing you to integrate with your preferred tools and platforms across the entire ML lifecycle.

Benefit from ZenML's open architecture, which lets you easily swap out components and avoid vendor lock-in, unlike ClearML's more rigid, all-in-one approach.

Leverage ZenML's extensive integration capabilities to create a customized ML stack that fits your specific needs, without being constrained to a single vendor's ecosystem.

Easy to Adopt

ZenML offers a straightforward setup process, requiring minimal infrastructure knowledge compared to ClearML's more complex agent-based architecture.

Deploy ZenML quickly and easily in various environments, from local development to cloud platforms, without the need for extensive configuration or dedicated infrastructure.

Enjoy a gentler learning curve with ZenML's intuitive API, allowing you to focus on your ML workflows rather than managing infrastructure or adapting to a specific vendor's paradigm.

Open Ecosystem with no lock-in

ZenML offers a wide range of integrations with popular MLOps tools, allowing you to create a best-of-breed stack tailored to your needs, unlike ClearML's more closed ecosystem.

Easily incorporate your preferred experiment tracking, model registry, feature store, and deployment platforms into your ML workflows with ZenML's flexible integration architecture.

Benefit from ZenML's active community and growing ecosystem of integrations, ensuring you can adapt your ML infrastructure as new tools and best practices emerge in the fast-moving MLOps landscape.

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 clearml.automation.controller import PipelineDecorator
from clearml import TaskTypes

@PipelineDecorator.component(return_values=["data_frame"], cache=True, task_type=TaskTypes.data_processing)
def step_one(pickle_data_url: str, extra: int = 43):
    print("step_one")
    import sklearn  # noqa
    import pickle
    import pandas as pd
    from clearml import StorageManager

    local_iris_pkl = StorageManager.get_local_copy(remote_url=pickle_data_url)
    with open(local_iris_pkl, "rb") as f:
        iris = pickle.load(f)
    data_frame = pd.DataFrame(iris["data"], columns=iris["feature_names"])
    data_frame.columns += ["target"]
    data_frame["target"] = iris["target"]
    return data_frame

@PipelineDecorator.component(
    return_values=["X_train", "X_test", "y_train", "y_test"], cache=True, task_type=TaskTypes.data_processing
)
def step_two(data_frame, test_size=0.2, random_state=42):
    print("step_two")
    import pandas as pd  # noqa
    from sklearn.model_selection import train_test_split

    y = data_frame["target"]
    X = data_frame[(c for c in data_frame.columns if c != "target")]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)

    return X_train, X_test, y_train, y_test

@PipelineDecorator.component(return_values=["model"], cache=True, task_type=TaskTypes.training)
def step_three(X_train, y_train):
    print("step_three")
    import pandas as pd  # noqa
    from sklearn.linear_model import LogisticRegression

    model = LogisticRegression(solver="liblinear", multi_class="auto")
    model.fit(X_train, y_train)
    return model

@PipelineDecorator.component(return_values=["accuracy"], cache=True, task_type=TaskTypes.qc)
def step_four(model, X_data, Y_data):
    from sklearn.linear_model import LogisticRegression  # noqa
    from sklearn.metrics import accuracy_score

    Y_pred = model.predict(X_data)
    return accuracy_score(Y_data, Y_pred, normalize=True)

@PipelineDecorator.pipeline(name="custom pipeline logic", project="examples", version="0.0.5")
def executing_pipeline(pickle_url, mock_parameter="mock"):
    print("pipeline args:", pickle_url, mock_parameter)

    print("launch step one")
    data_frame = step_one(pickle_url)

    print("launch step two")
    X_train, X_test, y_train, y_test = step_two(data_frame)

    print("launch step three")
    model = step_three(X_train, y_train)

    print("returned model: {}".format(model))

    print("launch step four")
    accuracy = 100 * step_four(model, X_data=X_test, Y_data=y_test)

    print(f"Accuracy={accuracy}%")

if __name__ == "__main__":
    PipelineDecorator.run_locally()

    executing_pipeline(
        pickle_url="https://github.com/allegroai/events/raw/master/odsc20-east/generic/iris_dataset.pkl",
    )

    print("process completed")

Experience the ZenML Difference: Elevate Your MLOps with Flexibility and Simplicity

Avoid Lock-In: Integrate your preferred tools with ZenML's open architecture
Simplified Setup: Get started quickly with minimal infrastructure knowledge
Customized Stack: Create a best-of-breed MLOps environment tailored to your needs
Future-Proof Workflows: Easily adapt and scale as your ML requirements evolve

See ZenML's superior model orchestration in action

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Streamline Your ML Workflows

Comprehensive MLOps Solution

Easy to Adopt

Open Ecosystem with no lock-in

ZenML allows you to quickly and responsibly go from POC to production ML systems while enabling reproducibility, flexibility, and above all, sanity

Explore in Detail What Makes ZenML Unique

Comprehensive MLOps Coverage

Flexibility and Customization

Focus on Usability and Adoption

Outperform Orchestrators: Book Your Free ZenML Strategy Talk

Broaden Your MLOps Understanding with ZenML

Experience the ZenML Difference: Book Your Customized Demo

Experience the ZenML Difference: Elevate Your MLOps with Flexibility and Simplicity

MLOps Integrations	Extensive integrations with various MLOps tools and platforms	Limited to ClearML's ecosystem of tools
Flexibility	Highly customizable with modular architecture	More rigid, all-in-one approach
Setup Complexity	Simple setup with minimal infrastructure knowledge required	More complex setup with agent-based architecture
Vendor Lock-in	Open architecture allows easy tool swapping and avoids lock-in	Tighter coupling with ClearML's ecosystem
Learning Curve	Intuitive API with gentle learning curve	Steeper learning curve due to specific paradigms
Collaboration	Strong collaboration features with version control and sharing	Good collaboration features within ClearML's platform
Scalability	Easily scalable across various environments	Scalable within ClearML's infrastructure
Monitoring	Comprehensive monitoring with integration options	Strong monitoring capabilities within ClearML

Streamline Your ML Workflows

Comprehensive MLOps Solution

Easy to Adopt

Open Ecosystem with no lock-in

ZenML allows you to quickly and responsibly go from POC to production ML systems while enabling reproducibility, flexibility, and above all, sanity

Comprehensive MLOps Coverage

Flexibility and Customization

Focus on Usability and Adoption

Outperform Orchestrators: Book Your Free ZenML Strategy Talk

Broaden Your MLOps Understanding with ZenML

Why you should be using caching in your machine learning pipelines

Transforming Vanilla PyTorch Code into Production Ready ML Pipeline - Without Selling Your Soul

Why ML should be written as pipelines from the get-go

Experience the ZenML Difference: Book Your Customized Demo

Experience the ZenML Difference: Elevate Your MLOps with Flexibility and Simplicity