Airflow vs MLflow: What are the differences?

Key Differences between Airflow and MLflow

Introduction

Airflow and MLflow are both popular open-source platforms used in data engineering and machine learning workflows. While they serve similar purposes, there are key differences between the two platforms. This article highlights the main differences between Airflow and MLflow.

1. Workflow Management vs. Model Management: Airflow is primarily a workflow management platform that focuses on managing and scheduling complex data pipelines. It provides task dependencies, parallel execution, and retry capabilities. MLflow, on the other hand, is designed for managing the machine learning lifecycle, including experiment tracking, reproducibility, and model deployment.

2. Orchestration vs. Experiment Tracking: Airflow excels in orchestrating and scheduling tasks across different systems. It provides a graphical interface to define, monitor, and manage workflows. MLflow, however, shines in experiment tracking and management. It allows data scientists to track experiments, log parameters, metrics, and artifacts, and reproduce past results.

3. Task Execution vs. Model Registry: In Airflow, each task represents a unit of work, which can be executed on different platforms or systems. It focuses on task execution and provides operators for various tasks, such as data ingestion, transformation, and processing. MLflow emphasizes the model registry, where you can register, version, and deploy machine learning models.

4. Pythonic vs. Language-Agnostic: Airflow is written in Python and supports Python-based tasks out of the box. While you can integrate other languages into Airflow, it is primarily a Python-based framework. MLflow, on the other hand, is designed to be language-agnostic. It supports multiple programming languages and frameworks, allowing data scientists to use their preferred tools for model development and training.

5. DAGs vs. Experiments: Airflow uses Directed Acyclic Graphs (DAGs) to define and represent workflows. DAGs provide a visual representation of tasks and their dependencies. MLflow, on the other hand, uses experiments as the central unit of work. Each experiment can have multiple runs, representing different iterations or versions of models.

6. Community and Ecosystem: Both Airflow and MLflow have vibrant communities and ecosystems. Airflow has been around since 2014 and has a large community of contributors, offering a wide range of plugins and integrations. MLflow, although relatively newer, has gained significant popularity, especially in the machine learning community, and also has a growing ecosystem of extensions and integrations.

In summary, Airflow is primarily a workflow management platform focused on task execution and orchestration, while MLflow is a tool designed specifically for machine learning lifecycle management, including experiment tracking and model registry. Airflow is Python-centric, while MLflow is language-agnostic. However, both platforms have their unique strengths and can be used together in data engineering and machine learning workflows.