as someone in the space this ticks a lot of boxes: kubernetes-native, strong isolation, python sdk (ideal for ML scenarios). devmapper is a nice ootb approach.
Glancing at the readme, is your business model technical support? Or what's your plan with this?
Anything interesting to share around startup time for large artifacts, scaling, passing through persistent storage (or GPUs) to these sandboxes?
Curious what things like 'Multi-node cluster capabilities for distributed workloads' mean exactly? inter-VM networking?
I’d argue there’s a big need for people with solid fundamental CS, sysadmin, infra skills who can bridge the gap into ML practitioner/researcher understanding. Applications or inference generally are probably easiest to break into, especially if you already have service knowledge. If you want to work on distributed training or kernel/model optimization, you probably need to prove your chops there.
Neoclouds, startups in the AI space, maybe hw vendors are probably good places to look.