Cortex
Production infrastructure for machine learning
Cortex is an open-source platform for machine learning (ML) model serving and orchestration. It is designed to help teams deploy and manage ML models at scale in production environments, with features that facilitate model versioning, scalability, and reliability. At its core, Cortex is a container-based platform that allows users to deploy and manage ML models using a simple and intuitive API. It supports a wide range of ML frameworks, including TensorFlow, PyTorch, and Scikit-Learn, and can be used to serve both online and batch predictions. One of the key features of Cortex is its support for model versioning and management. Users can easily deploy new versions of their models, manage multiple versions in parallel, and seamlessly switch between versions without downtime. Cortex also includes a range of features designed to ensure scalability and reliability in production environments. It can automatically scale up or down based on traffic, and includes support for horizontal scaling across multiple nodes. It also includes built-in support for monitoring, logging, and alerting, making it easy to keep track of the performance and health of deployed models. Overall, Cortex is a powerful platform for ML model serving and orchestration that can help teams to streamline the deployment and management of their ML models in production environments. Its open-source architecture and active community also make it a cost-effective alternative to commercial ML Ops solutions.