Seldon
Deployment & monitoring for machine learning at scale
Seldon is an open-source platform for managing machine learning models in production. It is specifically designed to help data scientists and machine learning engineers deploy and manage their models at scale, in a variety of production environments. Seldon provides a range of tools and features that make it easier to package, deploy, and monitor machine learning models. Some of its key features include: Model serving: Seldon provides a scalable, container-based serving layer that can handle large numbers of incoming requests. Model packaging: Seldon allows you to package your machine learning models as Docker containers, making it easy to deploy them in a variety of production environments. Model monitoring: Seldon provides real-time monitoring of your models, including performance metrics, usage statistics, and feedback on model accuracy. A/B testing: Seldon makes it easy to test multiple versions of your models in production, using techniques like A/B testing to evaluate their performance. Deployment automation: Seldon includes tools for automating the deployment and scaling of your models, making it easier to manage large-scale machine learning deployments. Seldon is built on top of Kubernetes, a popular container orchestration platform, which means it is designed to work well in modern, cloud-based environments. It is also designed to be highly modular, making it easy to extend and customize as needed. Overall, Seldon provides a powerful set of tools for managing machine learning models in production, and is a popular choice among data scientists and machine learning engineers.