Designing machine learning systems
A practical guide to the full lifecycle of machine learning systems — from aligning business and model metrics to shadow deployment, data drift monitoring, and continuous learning in production.
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Who it is for
For data engineers, ML scientists, and product leaders who want to stop treating models as isolated prototypes and start shipping reliable systems in the real world.
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