AI Ops and its challenges
summary
Claims a unified AI Ops layer that merges CMDB data, observability telemetry and configuration management to control hardware (power, cooling), schedule containers colocations and orchestrate multicloud moves. Reports a real migration of ~800 developers and ~500 products to new processes, presents open source Fage (released months ago) and Microsoft Radios as working stacks, says the AI-driven broker handles millions-to-billions of signals in real time and cuts integration cost enough to make HPC-style solutions practical.
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chapters (18)
- 0:00Introduction
- 0:51Defining AI Ops vs MLOps
- 1:57Modern infrastructure complexity
- 3:06Hardware and cloud evolution
- 4:56Three systems for infrastructure
- 6:45Observability and real-time control
- 8:13Configuration management limits
- 9:59CMDB and the missing unified infrastructure tool
- 11:02Supercomputing history relevance
- 12:53AI cost reduction and unification
- 14:20Fage and Radix solutions
- 16:51Fage with Kubernetes and hardware
- 17:58Real-time hardware performance
- 19:00User app and metric control
- 20:25Multicloud cost optimization
- 21:51Multicloud reliability and scaling
- 22:56AI Ops benefits and skills
- 23:39Q&A
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