Cortex vs Seldon
Detailed comparison between Cortex and Seldon: Cortex: Cortex is an open-source platform for deploying machine learning models in production. It is designed to make it easy to build, deploy, and manage machine learning models at scale. Cortex supports many different machine learning frameworks, including TensorFlow, PyTorch, and scikit-learn, and allows users to deploy models in a variety of different ways, including as REST APIs or serverless functions. Seldon: Seldon is an open-source platform for deploying machine learning models in production. It is designed to make it easy to build, deploy, and manage machine learning models at scale. Seldon supports many different machine learning frameworks, including TensorFlow, PyTorch, and scikit-learn, and allows users to deploy models in a variety of different ways, including as REST APIs or serverless functions. Here are some differences between Cortex and Seldon: Architecture: Cortex is designed as a standalone platform, which means that it can be deployed on any infrastructure, including public clouds, private clouds, and on-premise servers. Seldon, on the other hand, is built on top of Kubernetes, which means that it is tightly integrated with the Kubernetes ecosystem and requires a Kubernetes cluster to run. Model Serving: Cortex and Seldon both support model serving as REST APIs or serverless functions. However, Cortex provides more flexible serving options, such as gRPC, WebSockets, and Kafka, while Seldon focuses more on Kubernetes-native serving options. Inference Pipelines: Cortex provides support for building complex inference pipelines that can include multiple machine learning models, data pre-processing, and post-processing. Seldon also supports inference pipelines, but its pipeline functionality is more limited than Cortex's. Auto Scaling: Cortex provides built-in auto-scaling functionality, which allows the platform to automatically scale up or down based on the demand for a particular model. Seldon also supports auto-scaling, but it requires users to configure Kubernetes Horizontal Pod Autoscalers (HPAs) manually. Community: Cortex has a smaller user community than Seldon, which means that it may be more challenging to find support or contribute to the project. Seldon has a larger user community and is backed by several commercial organizations, including Google, Red Hat, and IBM. In summary, both Cortex and Seldon are excellent open-source platforms for deploying machine learning models in production. Cortex is more flexible in terms of deployment options and provides more advanced serving and inference pipeline functionality, while Seldon is more tightly integrated with the Kubernetes ecosystem and has a larger user community.