[calebkaiser]

You're 100% right that Cortex is designed for the production use-case. A lot of our users are running Cortex for "small" production use cases, since the Cortex cluster can include just a single EC2 instance for model serving (autoscaling allows deployed APIs to scale down to 1 replica). For ML use-cases that don't need an API (a lot of data analysis work, for example), Cortex is probably overkill.

As for your second question, we definitely want to integrate tightly with version control systems. Since right now we are 100% open source and don't offer a manged service, we don't have a place to run the webook listeners. That said, most of our users version control their code/configuration (we do that with our examples as well: https://github.com/cortexlabs/cortex/examples), and it should be straightforward to integrate Cortex into an existing CI/CD workflow; the Cortex CLI just needs to be installed, and then running `cortex deploy` with the updated code/configuration will trigger a rolling update.

If you're referring to version control for the actual model files, Cortex is un-opinionated as to where those hosted, so long as they can be accessed by your Predictor (what we call the Python file that initializes your model and serves predictions). If you're interested in implementing version control with your models, I'd recommend checking out DVC.

[cgoltsev]

Is it possible to partner with you to offer a managed service for Cortex? We are looking at your solution to offer our clients for deployment.

[itsderek23]

Great Caleb - makes sense. Thanks!