The original title is about seeking guidance on AI platform engineering focusing on distributed systems, scheduling, and GPU technologies.

Short summary

AI Platform Engineering's critical bottleneck lies in distributed systems and GPU resource scheduling—not machine learning itself. Kubernetes' default scheduler treats GPUs as generic resources, ignoring memory fragmentation and compute intensity, resulting in underutilization (often 30%) and job failures despite available hardware. Solutions include NVIDIA Device Plugin, custom schedulers, and careful orchestration of Ray workers and vLLM memory paging, but demand deep infrastructure expertise.

• •GPU scheduling is the real bottleneck in AI platforms, not ML algorithms themselves
• •Kubernetes lacks GPU awareness, causing 30% underutilization through memory fragmentation and thermal issues
• •Solutions like NVIDIA Device Plugin and custom schedulers exist but require deep distributed systems knowledge

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