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Building Better ML Systems — Chapter 4. Model Deployment and Beyond
Towards Data Science - Medium towardsdatascience.com
Building Better ML Systems — Chapter 4. Model Deployment and Beyond
About deployment, monitoring, data distribution drifts, model updates, and tests in production.
Deploying models and supporting them in production is more about engineering and less about machine learning.
When an ML project approaches production, more and more people get involved: Backend Engineers, Frontend Engineers, Data Engineers, DevOps, Infrastructure Engineers...
They choose data storages, introduce workflows and pipelines, integrate service into the backend and UI codebase, automate releases, …
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