The largest hidden costs are data engineering (cleaning, labeling, building pipelines), cloud infrastructure for training and inference, integration with existing business systems, compliance/security audits, and the ongoing MLOps work for monitoring and maintaining model performance over time. These costs emerge during live integration with messy production data streams.
The largest hidden costs are data engineering (cleaning, labeling, building pipelines), cloud infrastructure for training and inference, integration with existing business systems, compliance/security audits, and the ongoing MLOps work for monitoring and maintaining model performance over time. These costs emerge during live integration with messy production data streams.
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The largest hidden costs are data engineering (cleaning, labeling, building pipelines), cloud infrastructure for training and inference, integration with existing business systems, compliance/security audits, and the ongoing MLOps work for monitoring and maintaining model performance over time. These costs emerge during live integration with messy production data streams.
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