This certainly looks like a cleaner way to deploy an ML model than SageMaker. Couple of questions:
* Is this really for more intensive model inference applications that need a cluster? It feels like for a lot of my models, a cluster is overkill.
* A lot of the ML deployment (Cortex, SageMaker, etc) don't see to rely on first pushing changes to version control, then deploying from there. Is there any reason for this? I can't come up for a reason why this shouldn't be the default. For example, this is how Heroku works for web apps (and this is a web app at the end of the day).
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.
* Is this really for more intensive model inference applications that need a cluster? It feels like for a lot of my models, a cluster is overkill.
* A lot of the ML deployment (Cortex, SageMaker, etc) don't see to rely on first pushing changes to version control, then deploying from there. Is there any reason for this? I can't come up for a reason why this shouldn't be the default. For example, this is how Heroku works for web apps (and this is a web app at the end of the day).