MLflow vs Seldon: What are the differences?

Introduction:

In this markdown, we will highlight key differences between MLflow and Seldon, two popular platforms used for managing and deploying machine learning models.

1. Model Management Approach: MLflow focuses on the entire machine learning lifecycle, providing tools for experimentation, versioning, and deployment. It offers a centralized repository to track and manage models, and supports various frameworks and languages. Seldon, on the other hand, is more focused on model serving. It provides a platform for deploying and scaling machine learning models as REST microservices. Seldon simplifies model deployment by abstracting away the infrastructure and provides advanced deployment strategies like canary deployment and A/B testing.

2. Support for Model Interpretability: MLflow offers native support for model interpretability, allowing users to understand and explain the behavior of their models. It provides tools like integrated visualization of metrics and parameters, as well as a model registry to track and compare different versions. Seldon, although it doesn't offer built-in interpretability tools, can integrate with other interpretability libraries like OpenAI's interpretability library or IBM AI Explainability 360.

3. Integration with Kubernetes: Seldon is built on top of Kubernetes, a popular container orchestration platform. This allows for seamless integration with Kubernetes deployments, enabling scalability and fault tolerance. MLflow, on the other hand, does not have a direct dependency on Kubernetes but can be deployed on Kubernetes using custom configurations.

4. Online Real-time Scoring: Seldon specializes in real-time prediction serving. It facilitates real-time scoring by providing a RESTful interface for model inference, which allows applications to query models in real-time. MLflow, on the other hand, is more geared towards offline batch scoring and model training, although it can also be used for real-time serving with custom integrations.

5. Model Versioning and Reproducibility: MLflow emphasizes versioning and reproducibility in the machine learning workflow. It provides the capability to track and compare different versions of a model and reproduce results by storing the associated code, data, and parameters. Seldon, while it supports versioning of models, does not offer built-in features for comprehensive version control and reproducibility.

6. Enterprise-Grade Features: MLflow offers additional enterprise-grade features like advanced security and access controls, centralized logging, and integration with authentication providers. These features are particularly useful in larger organizations with strict compliance requirements. Seldon also provides enterprise features but with a focus on scalability and improved monitoring capabilities.

In Summary, MLflow focuses on the entire machine learning lifecycle and offers model interpretability, while Seldon specializes in real-time serving, supports Kubernetes integration, and provides advanced deployment strategies.