Deploying machine learning models
Deploying machine learning models refers to the process of making a trained model available for making predictions on new, unseen data. It involves taking the trained model and integrating it into a production environment where it can receive input data, perform predictions, and return the results. The trained model needs to be organized and packaged into a format suitable for deployment. This may involve exporting the model into a file format that can be easily loaded and used by other systems. An application programming interface (API) is typically created to expose the machine learning model’s functionality. The API acts as the interface that other systems or applications can use to send data and receive predictions from the model.
If the model is expected to handle many concurrent requests, the deployment environment may need to be scaled to accommodate the increased load. This may involve setting up clusters of servers or using cloud...
