Cortex Labs Simplifies Machine Learning Model Deployment for Data Scientists
Cortex Labs provides open-source tooling that helps data scientists easily deploy machine learning models into production without needing infrastructure expertise. The startup eventually plans to offer a multi-cloud managed service to compete with AWS SageMaker.
Cortex Labs offers an open-source solution that helps data scientists deploy machine learning models into live applications without requiring deep infrastructure knowledge. The founding team combines popular open-source tools like TensorFlow, Kubernetes, and Docker with AWS services such as EKS and S3 to create a single API for seamless model deployment.
The deployment process involves a simple workflow where users upload an exported model file to S3, and the platform automatically handles containerization, Kubernetes deployment, workload scaling, and GPU switching for compute-intensive tasks. While this approach currently resembles Amazon SageMaker, Cortex differentiates itself by planning to support all major cloud platforms instead of being locked into a single provider.
The startup currently sustains itself with an $888,888 seed round raised in 2018 and focuses on building a strong community around its open-source tools. In the future, Cortex plans to monetize its efforts by launching a fully managed cloud service designed for companies that prefer not to manage their own infrastructure clusters.