How to deploy machine learning models

The life cycle of a machine learning model starts with data collection and ends with deployment and monitoring. The following describes various ways to deploy models into the environment.

Edge deployment deploys models directly to applications or IoT devices, but is limited by local device resources, and scale and efficiency will be limited.

2.Web service: This is the most widely used deployment method. The model is encapsulated using a REST API, and the prediction results are obtained through HTTP calls to the API endpoint.

3. Database integration: For occasional updates to small databases, ML models can be deployed in the database. The database server supports Python script integration, also suitable for model deployment.

The way a model is deployed depends on a variety of conditions. When it comes to regulatory or privacy issues with data storage, it is common to choose to deploy models within the application for security reasons. And when you need to serve multiple devices (such as mobile devices, web, and desktop), connecting the model with a web service is more effective than deploying it separately on each device. This enables centralized management and maintenance of the model while reducing device resource usage.

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