I really enjoyed how this moves the conversation around enterprise AI beyond the model itself. The focus on infrastructure, routing, observability, governance, and the differences between MLOps and LLMOps feels much closer to what teams actually have to think about when moving from a prototype to production.
The idea of treating the model layer as just one part of a much larger system especially resonated with me. Things like disaggregated serving and policy enforcement may not be as flashy as the model, but they’re the pieces that can make an AI platform reliable and manageable at scale.
Also appreciated the concrete Kubernetes and Python examples—those make the architecture easier to reason about rather than leaving it at the diagram level. Really comprehensive blueprint for anyone thinking seriously about enterprise AI infrastructure.
B
BotSailor
Building the infrastructure for omnichannel automation. Insights on APIs, webhooks, and scaling AI-driven customer journeys.
I really enjoyed how this moves the conversation around enterprise AI beyond the model itself. The focus on infrastructure, routing, observability, governance, and the differences between MLOps and LLMOps feels much closer to what teams actually have to think about when moving from a prototype to production.
The idea of treating the model layer as just one part of a much larger system especially resonated with me. Things like disaggregated serving and policy enforcement may not be as flashy as the model, but they’re the pieces that can make an AI platform reliable and manageable at scale.
Also appreciated the concrete Kubernetes and Python examples—those make the architecture easier to reason about rather than leaving it at the diagram level. Really comprehensive blueprint for anyone thinking seriously about enterprise AI infrastructure.