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What the hell is post-deployment data science?
Post-deployment data science is all the work done on a model after it has been deployed in production. This means monitoring model performance, business impact, understanding feedback loops, and optimizing the model to perform better over time.
Model Monitoring is reactive. It tracks data drift, concept drift, and performance changes after they have happened and attempts to explain them. In post-deployment data science, you try to make data science as proactive as possible in addressing possible issues using ML models in production.
nannyML
This newsletter is curated by Santiago Víquez, ML Developer Advocate at nannyML. nannyML that allows to estimate your model performance in production even without ground-truth.
