I was an early member of the CNCF community (circa 2016), and at the time I thought "wow things are moving quickly." Lots of different tech was being introduced to solve similar problems -- I distinctly remember multiple ways of templating K8S YAML :-).
Now that I'm spending time learning AI, it feels the same -- but the innovation pace feels at least 10x faster than the evolution of the cloud native ecosystem.
At this point, there's a reasonable degree of convergence around the core abstractions you should start with in the cloud-native world, and an article written today on this would probably be fine a year from now. I doubt this is the case in AI.
(Caveat: I've only been learning about the space for about 4 weeks, so maybe it's just me!)
> At this point, there's a reasonable degree of convergence around the core abstractions you should start with in the cloud-native world, and an article written today on this would probably be fine a year from now. I doubt this is the case in AI.
It's a continuous process. It is way, way, way better than it was 8 years ago. Most of the frameworks can export models between each other/delta some layers, ONNX actually largely kinda works.
Now that I'm spending time learning AI, it feels the same -- but the innovation pace feels at least 10x faster than the evolution of the cloud native ecosystem.
At this point, there's a reasonable degree of convergence around the core abstractions you should start with in the cloud-native world, and an article written today on this would probably be fine a year from now. I doubt this is the case in AI.
(Caveat: I've only been learning about the space for about 4 weeks, so maybe it's just me!)