They fail because they're treated as R&D projects rather than integrated production systems. The critical handover from data science teams to platform engineering teams often breaks down, and retrieval pipelines lack proper DevOps and observability. Real query loads expose these weaknesses, and governance overhead for sensitive data in regulated industries can add 6-12 months of delay.
They fail because they're treated as R&D projects rather than integrated production systems. The critical handover from data science teams to platform engineering teams often breaks down, and retrieval pipelines lack proper DevOps and observability. Real query loads expose these weaknesses, and governance overhead for sensitive data in regulated industries can add 6-12 months of delay.
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They fail because they're treated as R&D projects rather than integrated production systems. The critical handover from data science teams to platform engineering teams often breaks down, and retrieval pipelines lack proper DevOps and observability. Real query loads expose these weaknesses, and governance overhead for sensitive data in regulated industries can add 6-12 months of delay.
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