Compare ZenML vs

ZenML vs MLflow: Streamline Your ML Workflows

Discover how ZenML and MLflow approach machine learning lifecycle management differently. While MLflow focuses on experiment tracking, model deployment, and maintaining a centralized model registry, ZenML offers a comprehensive end-to-end MLOps framework. ZenML empowers you to effortlessly orchestrate entire ML pipelines, integrate with various tools including MLflow, and provides flexibility across different environments. Experience enhanced workflow management, reproducibility, and efficiency as you navigate the ML lifecycle with ZenML's unified approach, going beyond just experiment tracking to cover the full spectrum of MLOps needs.

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Pipelines as experiments

ZenML is built on top of the idea of steps and pipelines, which play together nicely with experiment tracking tools.
Experiment tracking tools like MLflow are good to track your training runs and hyperparameters while pipelines automate runs.
ZenML is a management layer on top of your ML experiments, and manages their lifecycle within the context of a pipeline.

Dashboard mockup showing local-to-production workflow

Connect your experiment trackers with your infrastructure securely

ZenML connectors can be used to establish a secure connection to your production infrastructure with your experiment tracker.
Keeps the complexity of authentication and authorization away from your code.
ZenML establishes a link between your experiment trackers and your entire MLOps process.

Dashboard mockup showing portability features

Provide lineage and provenance for your experiments

Experiment trackers focus mostly on training — while MLOps stretches beyond that.
ZenML provides an overview of the entire process from feature engineering, to training, to deployment, inference and beyond.
ZenML can be paired nicely with experiment trackers to provide full reproducibility and auditability over multiple tracking tools.

Dashboard mockup showing data versioning and lineage

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

Goku Mohandas

Founder of MadeWithML

Feature-by-feature comparison

Explore in Detail What Makes ZenML Unique

Feature

ZenML

MLflow

Experiment Tracking	Integrates with MLflow for detailed experiment tracking	Full experiment tracking capabilities
Model Registry	Utilizes MLflow's model registry for model versioning and management as part of full lifecycle	Offers a centralized model registry for model versioning and management
Model Deployment	Simplifies model deployment with MLflow integration	Supports model deployment to various platforms
Pipeline Orchestration	Provides a flexible and extensible pipeline orchestration framework	Limited built-in pipeline orchestration capabilities
Data Versioning	Comprehensive data versioning and management	No built-in data versioning functionality
Workflow Management	End-to-end workflow management for the entire ML lifecycle	Focuses primarily on experiment tracking, model registry, and deployment
Tool Integration	Seamlessly integrates with a wide range of MLOps tools, including MLflow	Integrates with various ML frameworks and libraries
Collaboration	Facilitates collaboration among team members throughout the ML lifecycle	Enables collaboration through experiment tracking and model sharing
Customizability	Highly customizable and extensible to fit specific project requirements	Customizable to some extent, but may require additional development effort
UI/UX	Provides an intuitive and user-friendly interface for managing ML workflows	Offers a web-based UI for experiment tracking and model management
Community and Support	Growing community with active support and resources	Large and active community with extensive resources and support
Scalability	Designed to scale with the growth of your ML projects and team	Scalable experiment tracking and model registry capabilities
MLOps Coverage	Covers the entire MLOps lifecycle, from data preparation to model monitoring	Focuses primarily on experiment tracking, model registry, and deployment
Integration Flexibility	Allows integration with various orchestrators, data stores, and deployment targets	Integrates with some popular ML frameworks and libraries
Learning Curve	Easy to set up and get started, but supports the full complexity and features offered by MLflow	Relatively easy to get started with, especially for experiment tracking

Code comparison

ZenML and MLflow side by side

ZenML

# zenml integration install mlflow
# zenml experiment-tracker register mlflow_tracker -f mlflow ...

from zenml import pipeline, step
from zenml.integrations.mlflow.experiment_trackers import MLFlowExperimentTracker
from zenml.client import Client

@step(experiment_tracker="mlflow_tracker")
def train_model(X_train, y_train, X_test, y_test):
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import accuracy_score

    model = RandomForestClassifier()
    model.fit(X_train, y_train)

    # ZenML automatically logs parameters and the model
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    
    # Only need to log custom metrics
    return model, {"accuracy": accuracy}

@pipeline
def ml_pipeline():
    X, y = load_data()
    X_train, X_test, y_train, y_test = preprocess_data(X, y)
    model, metrics = train_model(X_train, y_train, X_test, y_test)

ml_pipeline()

MLflow

# MLflow manual usage
import mlflow
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("my_experiment")

with mlflow.start_run():
    # Load and preprocess data
    X, y = load_data()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

    # Train model
    model = RandomForestClassifier()
    model.fit(X_train, y_train)

    # Log parameters
    mlflow.log_param("n_estimators", model.n_estimators)
    mlflow.log_param("max_depth", model.max_depth)

    # Log metrics
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    mlflow.log_metric("accuracy", accuracy)

    # Log model
    mlflow.sklearn.log_model(model, "random_forest_model")

# Retrieve and print the run ID
current_run = mlflow.active_run()
print(f"MLflow Run ID: {current_run.info.run_id}")

Comprehensive MLOps Coverage

ZenML provides an end-to-end MLOps solution, covering the entire lifecycle from data versioning to model monitoring, while MLflow focuses primarily on experiment tracking, model registry, and deployment.

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.

Collaborative Workflow Management

ZenML enables efficient collaboration among team members throughout the ML lifecycle, providing intuitive interfaces and workflow management features. While MLflow facilitates collaboration through experiment tracking and model sharing, it lacks the comprehensive workflow management capabilities offered by ZenML.