Day 60 of #60daysOfMachineLearning
🔷 Model Serving 🔷
Model serving refers to the process of deploying a trained machine learning model in a production environment and using it to make predictions on new data.
🔷 Model Serving 🔷
Model serving refers to the process of deploying a trained machine learning model in a production environment and using it to make predictions on new data.
There are several considerations to keep in mind when serving a machine learning model.
One important consideration is the performance of the model. The model should be able to make predictions quickly and accurately in order to meet the needs of the application.
Another important consideration is the scalability of the model. If the model is going to be used to make predictions on a large number of data points, it should be able to handle the workload without performance degradation.
It is also important to consider the maintenance of the model. The model may need to be retrained periodically as the underlying data or the business problem changes.
There are several ways to serve a machine learning model, including deploying it as a web service or integrating it into an application using a machine learning API.
It is important to carefully plan and test the model serving process to ensure that the model is able to meet the needs of the application and the expectations of the users.
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