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5 Model Deployment Mistakes That Can Cost You a Lot
In Data Science projects, model deployment is probably the most critical and complex part of the whole lifecycle. Operational or mission-critical ML requires thorough design. You have to think about artifacts lineage and tracking, automatic deployments to avoid human errors, testing, and quality checks, feature availability when the model is online… and many more things. […]
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