ML engineers, data architects, and technical leads responsible for designing or modernizing the data infrastructure that supports AI/ML workloads across traditional, generative, and agentic AI initiatives, including teams navigating GPU utilization, MLOps tooling selection, and data lakehouse architecture decisions.
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The "Starving GPU Problem" occurs when storage and network cannot serve training data fast enough to fully utilize GPUs. As GPU generations advance from A100 (0.312 PFLOPS) to B200 (4.5 PFLOPS), the performance gap between compute and storage widens, making storage infrastructure a critical AI investment decision.
RAG and LLM fine-tuning have fundamentally different security implications: fine-tuning makes document-level authorization impossible after the fact, while RAG retrieves document snippets at inference time, preserving the ability to enforce per-user access controls.
The data infrastructure first approach covers building data lake, OTF-based data warehouse, MLOps tooling, and distributed training cluster before scaling model complexity, producing a foundation that supports traditional, generative, and agentic AI workloads alongside existing OLAP workloads on the same infrastructure.
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