kerv

Title says it all, but we're getting serious into deep learning, and I'm wondering how other organizations are managing their team cost wise in order to provide developers with the proper resources in order to do their jobs. Things like powerful GPUs and VMs can add up after a while. What are some of you doing to keep costs down while still empowering your employees to do their jobs effectively?

Hi,

I'm a bit out of the loop on this, but can someone share any information they might have on how Kubernetes orchestrated apps are typically deployed? We do quite a bit of B2B sales and have built a number of docker containers which do various things. We deploy these internally to our own environment via terraform scripts. We are now playing around with the idea of sharing this app with other customers (on-premise installs) and need some guidance.

Is there a standard that people follow/IT people expect to see for deployment of these types of apps? What is realistic of us to ask our customers to do when installing our application on their own servers? Some of these users might not even have a Kubernetes environment setup in their cloud/servers.

Thanks