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BEGIN:VEVENT
UID:pretalx-cloud-native-rejekts-atlanta-na-atlanta-2025-QKCFNE@cfp.cloud-n
 ative.rejekts.io
DTSTART;TZID=EST:20251108T111000
DTEND;TZID=EST:20251108T114000
DESCRIPTION:What does operational overhead look like in the era of MLOps? I
 f you're grappling with this question\, like many others\, and would like 
 a way to apply the paradigm of containers and cloud native to AI workloads
  ― you're in luck. \nThere is an effort underway to align AI workloads w
 ith the knowledge we have of operational excellence in cloud native. The C
 NCF Sandbox project ModelSpec brings much needed clarity to MLOps workflow
 s. It provides the right abstraction to be able to define how DevOps and c
 loud native practices can be applied for machine learning operations. \nAP
 plying the ModelSpec is the KitOps tool. It helps bridge the gaps that cur
 rently exist in the tooling space for MLOps. It creates a "Docker"-like in
 terface for AI workloads and makes it easy and efficient to work with mode
 ls on Kubernetes (or other container runtimes).\nIn this talk\, I aim to b
 ring together the ML overhead\, how cloud native paradigms can help\, the 
 ModelSpec\, and KitOps. Together\, all these will help expose an important
  painpoint in productionalizing AI in the workplace. Let's eliminate all t
 he disconnected ways in which data teams\, developers\, and operations fol
 ks are working by using the principles that will be highlighted during thi
 s talk.
DTSTAMP:20261013T134945Z
LOCATION:Theater
SUMMARY:Ease The Move From DevOps to MLOps: A Case For ModelSpec + KitOps -
  Ram Iyengar
URL:https://cfp.cloud-native.rejekts.io/cloud-native-rejekts-atlanta-na-atl
 anta-2025/talk/QKCFNE/
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