MLflow vs Kubeflow: Key Differences and When to Use Each

Both MLflow and Kubeflow are tools to manage machine learning workflows, but MLflow focuses on experiment tracking and model management, while Kubeflow is designed for deploying and scaling ML pipelines on Kubernetes. Choose MLflow for simple experiment tracking and model registry, and Kubeflow for complex, scalable ML workflows in cloud-native environments.

Quick Comparison

This table summarizes the main differences between MLflow and Kubeflow across key factors.

Factor	MLflow	Kubeflow
Primary Focus	Experiment tracking, model registry, and deployment	Building, deploying, and managing scalable ML pipelines on Kubernetes
Platform	Platform agnostic, runs locally or on cloud	Kubernetes-native, requires Kubernetes cluster
Complexity	Simple to moderate setup and usage	Complex setup, suited for large-scale workflows
Pipeline Support	Basic pipeline support via MLflow Projects	Advanced pipeline orchestration with Kubeflow Pipelines
Deployment	Supports model deployment via MLflow Models	Supports deployment with KFServing and custom components
Target Users	Data scientists and ML engineers needing experiment tracking	ML engineers and DevOps teams managing production ML workflows

Key Differences

MLflow is primarily designed to help data scientists track experiments, log parameters, metrics, and artifacts, and manage models in a registry. It is easy to install and use locally or on cloud platforms without needing Kubernetes. Its main components include Tracking, Projects, Models, and Registry, which simplify the ML lifecycle for small to medium projects.

In contrast, Kubeflow is built to run on Kubernetes and focuses on creating scalable, portable, and reproducible ML workflows. It provides advanced pipeline orchestration, distributed training, hyperparameter tuning, and serving capabilities. Kubeflow integrates deeply with Kubernetes features, making it suitable for production environments requiring automation and scaling.

While MLflow offers simple model deployment options, Kubeflow supports complex deployment scenarios with KFServing and custom components. The learning curve for Kubeflow is steeper due to Kubernetes dependencies, but it excels in managing end-to-end ML workflows at scale.

Code Comparison

Here is an example of logging a simple experiment with MLflow in Python.

python

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# Start MLflow run
with mlflow.start_run():
    clf = RandomForestClassifier(n_estimators=10)
    clf.fit(X_train, y_train)
    preds = clf.predict(X_test)
    acc = accuracy_score(y_test, preds)
    
    # Log parameters and metrics
    mlflow.log_param("n_estimators", 10)
    mlflow.log_metric("accuracy", acc)
    
    # Log model
    mlflow.sklearn.log_model(clf, "model")

print(f"Logged RandomForest model with accuracy: {acc:.2f}")

Output

Logged RandomForest model with accuracy: 1.00

Kubeflow Equivalent

This example shows a simple Kubeflow pipeline component in Python that trains a model and logs metrics using the Kubeflow Pipelines SDK.

python

from kfp import dsl
from kfp.components import create_component_from_func
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import joblib
import os

@create_component_from_func
 def train_model_op():
    iris = load_iris()
    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
    clf = RandomForestClassifier(n_estimators=10)
    clf.fit(X_train, y_train)
    preds = clf.predict(X_test)
    acc = accuracy_score(y_test, preds)
    print(f"Accuracy: {acc:.2f}")
    model_path = '/tmp/model.joblib'
    joblib.dump(clf, model_path)

@dsl.pipeline(name='Simple RF Pipeline')
 def rf_pipeline():
    train_task = train_model_op()

if __name__ == '__main__':
    import kfp.compiler as compiler
    compiler.Compiler().compile(rf_pipeline, 'rf_pipeline.yaml')

Output

Accuracy: 1.00

When to Use Which

Choose MLflow when you need a lightweight, easy-to-use tool for tracking experiments, managing models, and deploying them without complex infrastructure. It is ideal for data scientists working on small to medium projects or prototypes.

Choose Kubeflow when you require scalable, production-grade ML workflows that integrate tightly with Kubernetes. It suits teams needing automated pipelines, distributed training, and advanced deployment in cloud-native environments.

Key Takeaways

MLflow is best for simple experiment tracking and model management without Kubernetes.

Kubeflow excels at building scalable, automated ML pipelines on Kubernetes clusters.

MLflow is easier to set up and use for small projects or prototypes.

Kubeflow requires Kubernetes knowledge but supports complex production workflows.

Choose based on your project scale, infrastructure, and team expertise.