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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 several steps. First, the model needs to be packaged in a way that makes it easy to use within the production environment. This might involve converting the model into a format that can be easily loaded by the production system, or creating an API that allows other systems to interact with the model. Another important consideration when deploying a model is monitoring its performance in the production environment. This involves keeping track of how the model is performing over time and making adjustments as needed. This might involve retraining the model on new data, or updating the model with new features.- Model deployment is a critical step in the machine learning process. It allows the model to be used in real-world scenarios, where it can provide valuable insights and predictions. By integrating the model into a production system, it becomes a powerful tool that can help drive decision-making and improve outcomes.
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