DVC vs MLflow: What are the differences?

Introduction

DVC and MLflow are two popular tools in the field of machine learning that help manage and track experiments, models, and data. While both serve similar purposes, they have distinct differences that set them apart. In this article, we will explore the key differences between DVC and MLflow in 6 specific aspects.

1. Data Versioning: DVC primarily focuses on managing the versions of data used in machine learning projects. It allows users to track the changes made to datasets, maintain reproducibility, and easily switch between different data versions. On the other hand, MLflow does not provide native support for data versioning.

2. Model Versioning: MLflow is specifically designed to manage model versions. It provides a comprehensive framework to track and log models, including the ability to register and serve models in various deployment environments. While DVC can track models by treating them as regular files, it lacks the advanced model management features of MLflow.

3. Experiment Tracking: MLflow offers powerful experiment tracking capabilities, allowing users to record and organize experiments, parameters, metrics, and artifacts. It provides a centralized interface to compare and visualize experiment results. DVC, on the other hand, focuses more on the data and model versioning aspect and does not offer dedicated experiment tracking functionalities.

4. Workflow Orchestration: DVC provides a data-centric workflow orchestration system. It allows users to define dependencies between stages of a workflow based on data changes and execute them efficiently. MLflow, on the other hand, does not provide built-in workflow orchestration capabilities.

5. Integration with ML Frameworks: MLflow integrates seamlessly with popular machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. It provides APIs to log models, metrics, and artifacts directly from these frameworks. DVC, on the other hand, is framework-agnostic and can be used with any machine learning framework.

6. Deployment and Serving: MLflow provides built-in deployment and serving capabilities for machine learning models. It supports various serving options, such as running models as REST APIs or deploying them to cloud platforms like Azure ML and AWS SageMaker. DVC, on the other hand, focuses on the data and model versioning aspect and does not provide native deployment and serving functionalities.

In summary, DVC is primarily focused on data and model versioning, workflow orchestration, and framework-agnostic integration, while MLflow offers comprehensive capabilities for model versioning, experiment tracking, deployment, and serving of machine learning models.