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That makes sense. Programmatically updating cortex.yaml is a common use case especially when you're thinking about continuous deployment. We also have a Python client which can rep…

Hey Aaron, I work on Cortex which is a tool for continuously deploying models as HTTP endpoints on AWS. Under the hood we use Kubernetes instead of Lambda to avoid cold starts, ena…

From the MLflow Models docs: "An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools—for example, real-time se…

Each model is loaded into a Docker container, along with any Python packages and request handling code. The cluster runs on EKS on your AWS account. Cortex takes the declarative co…

Yes, Cortex uses ONNX Runtime ( https://github.com/microsoft/onnxruntime ) under the hood so any model that can be exported to ONNX can be deployed.

Good idea, I definitely think it's doable.

The Terraform provider idea is interesting, I'll think about it more carefully. Almost all of our deployment configuration under the hood is done with Kubernetes (which is focused …

My understanding is that Seldon and Kubeflow are more geared towards infrastructure engineers. Our goal is to hide the infrastructure tooling so that Kuberentes, Docker, or AWS exp…

Thanks for the feedback! We aren't trying to invent another infrastructure provisioning language, and I agree that Terraform would be the right choice if that was the case. Our YAM…

We solve a similar problem to SageMaker but we are focused on developer experience and flexibility. - Deployments are defined with declarative configuration and no custom Docker im…