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Model deployment involves integrating machine learning models into production systems from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Model deployment is a crucial step in the machine learning pipeline. This process involves taking the trained model and integrating it into a production system where it can make predictions on new data. Once a model has been trained and evaluated, it needs to be deployed in order to be useful. This means that the model is made available to others to use for making predictions. The goal of deployment is to make sure that the model can be used in a real-world setting, where it can generate predictions in response to new data. Integrating a machine learning model into a production system involves severa...
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