Metarank vs Seldon
Comparison between Metarank and Seldon: Metarank: Metarank is an open-source project for building scalable and interpretable recommender systems. It provides various models for collaborative filtering and content-based filtering, along with techniques for explainability and interpretability. It is built on top of Apache Spark and provides an API for easy integration with other systems. Metarank is focused on making it easy to build and evaluate different recommender system models. Seldon: Seldon is an open-source platform for building and deploying machine learning models at scale. It provides various tools for model deployment, monitoring, and management, along with techniques for explainability and interpretability. Seldon is built on top of Kubernetes and provides an API for easy integration with other systems. Seldon is focused on making it easy to deploy and manage machine learning models in production environments. In summary, while both Metarank and Seldon provide techniques for explainability and interpretability, they differ in their focus. Metarank is focused on building and evaluating different recommender system models, while Seldon is focused on deploying and managing machine learning models in production environments. Additionally, Metarank is built on Apache Spark, while Seldon is built on Kubernetes.