cortexlabs/cortex

Production infrastructure for machine learning at scale

45
/ 100
Emerging

This infrastructure helps machine learning engineers and MLOps teams deploy, manage, and scale their machine learning models in a production environment. It takes trained models and configurations, then makes them available as real-time APIs, asynchronous processors, or batch jobs, handling the underlying scaling and resource management on AWS. This is for teams who need to move their ML models from development to a robust, scalable, and observable production system.

8,027 stars. No commits in the last 6 months.

Use this if you are an MLOps engineer or machine learning practitioner needing to deploy trained models reliably and scalably on AWS, whether for real-time predictions, asynchronous processing, or batch jobs.

Not ideal if you are looking for a platform that is actively maintained by its original authors or if you need to deploy machine learning models outside of AWS.

machine-learning-operations model-deployment production-ML cloud-infrastructure ML-scalability
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 19 / 25

How are scores calculated?

Stars

8,027

Forks

598

Language

Go

License

Apache-2.0

Last pushed

Jun 12, 2024

Commits (30d)

0

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