Cortex - Deploy machine learning models to production

Model serving infrastructure

Scales to handle production workloads with request-based autoscaling

Ensures high availability with availability zones and automated instance restarts

Runs inference on spot instances with on-demand backups

Manages traffic splitting for A/B testing

Supports realtime and batch workloads

Deploy TensorFlow, PyTorch, sklearn and other models

Implement request handling in Python

Customize compute, autoscaling, and networking for each API

Package dependencies, code, and configuration for reproducible deployments

Test locally before deploying to your cluster

Monitor API performance

Aggregate and stream logs

Customize prediction tracking

Update APIs without downtime

Integrate with your existing stack