Framed Data vs Seldon: What are the differences?

**Key Differences Between Framed Data and Seldon**

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1. **Deployment Approach**: Framed Data primarily focuses on deploying machine learning models as RESTful APIs, whereas Seldon offers a more comprehensive deployment platform that includes support for Kubernetes-based deployments, serving, monitoring, scaling, and explanation of models.
2. **Model Explainability**: Seldon provides built-in support for model interpretability and explainability through advanced algorithms and visualization techniques, while Framed Data lacks this feature, focusing more on model serving and scalability.
3. **Model Monitoring**: Seldon offers robust model monitoring capabilities, allowing users to track the health and performance of deployed models, while Framed Data does not provide in-depth monitoring functionality, which can be a limitation in production environments.
4. **Model Scaling**: Seldon enables automatic scaling of models based on incoming traffic and resource utilization, ensuring efficient use of computational resources, whereas Framed Data may require manual intervention for scaling models as the workload increases.
5. **Model Versioning**: Seldon supports versioning of models, making it easier to manage and control different iterations of models over time, while Framed Data may have limitations in this area, potentially causing challenges in maintaining model version history.
6. **Community and Support**: Seldon has a larger community and active support system in place, providing users with resources, forums, and documentation to address any issues or questions, whereas Framed Data may have a smaller support network, leading to potential delays in resolving issues.

In Summary, Seldon offers a more comprehensive deployment platform with advanced features such as model explainability, monitoring, scaling, versioning, and robust community support compared to Framed Data's focus on model serving and RESTful API deployment.