Compare ZenML vs
MLflow

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.
ZenML
vs
MLflow

Comprehensive MLOps Platform

  • ZenML offers a complete MLOps platform that seamlessly integrates with MLflow and other essential tools.
  • With ZenML, you can manage the entire ML lifecycle, from data versioning to model deployment, within a single framework.
  • MLflow, while powerful, focuses primarily on experiment tracking, model registry, and deployment, requiring additional tools for end-to-end MLOps.
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Flexibility and Extensibility

  • ZenML's modular architecture allows you to easily integrate MLflow alongside other best-of-breed tools in your ML workflows.
  • With ZenML, you can customize and extend your pipelines to fit your specific requirements, ensuring a tailored MLOps experience.
  • MLflow, as a standalone tool, may require more effort to integrate with other components in your ML ecosystem.

Collaborative Workflow Management

  • ZenML enables seamless collaboration among data scientists, ML engineers, and other stakeholders throughout the ML lifecycle.
  • With ZenML's intuitive interface and workflow management features, teams can efficiently work together on ML projects, reducing friction and increasing productivity.
  • While MLflow enables collaboration through experiment tracking and model sharing, it lacks the comprehensive workflow management capabilities provided by ZenML.
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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
ZenML
MLflow
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

# 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

# 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.

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