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Also known as: cortex.dev, Cortex Labs
An open-source platform for deploying and scaling machine learning models as production APIs.
DESCRIPTION
Cortex is an open-source system that automates provisioning and scaling of inference APIs on cloud infrastructure. It packages models with Docker, deploys to Kubernetes, and manages autoscaling, logging, and monitoring. Teams use Cortex to serve models with REST endpoints, handling rollout strategies and resource utilization. It provides a path from notebooks to production-grade serving.
ALTERNATIVE DEFINITIONS
Cortex Labs (GitHub)
Cortex is an open source platform for deploying machine learning models in production.
Concise statement of purpose focused on production deployment.
Cortex Documentation
Deploy machine learning models to production with ease using Cortex’s scalable, cost-effective inference APIs.
Emphasizes scalability and ease for production inference.
RELATED TERMS
Model Serving Kubernetes Docker Autoscaling Amazon Web Services REST API Inference
COMPETING TERMS
KNOWLEDGE AREAS
MLOps Infrastructure Platform & Tooling Cloud
TARGET AUDIENCE
MLOps Engineer AI Engineer Data Engineer Cloud Architect Software Developer
EXTERNAL SOURCES
- Cortex Labs - Source code and README
- Cortex - Official documentation
TOP QUESTIONS
What problems does Cortex solve for MLOps?
It automates serving infrastructure—containerization, autoscaling, and endpoints—reducing the gap between models and production.
Category: Relevancy
How does Cortex scale model serving?
It uses Kubernetes to scale replicas based on traffic and resource metrics, optimizing cost and reliability.
Category: Performance
What’s a common mistake when adopting Cortex?
Neglecting resource requests and limits can cause instability. Proper sizing and autoscaling policies are crucial.
Category: Mistakes
How do I monitor models deployed with Cortex?
Integrate with logging and metrics backends; track latency, error rates, and resource usage for SLOs.
Category: HowTo
Can Cortex handle GPU inference?
Yes. It provisions GPU-backed instances and schedules GPU workloads on Kubernetes when configured.
Category: Capabilities
How does Cortex compare to BentoML and Seldon Core?
All serve models; Cortex leans into Kubernetes-managed autoscaling and cloud provisioning, while BentoML focuses on packaging, and Seldon on model graphs.
Category: Comparison
What deployment targets does Cortex support?
It primarily targets cloud Kubernetes clusters, especially on AWS, using Docker images.
Category: Requirements
How do I roll out new model versions in Cortex?
Use rolling updates or blue-green strategies, validating metrics to ensure performance and stability.
Category: HowTo
Is Cortex suitable for small teams?
Yes, if they need production-grade serving with Kubernetes. Managed alternatives may be simpler for minimal ops.
Category: Opinion