Integrating Model Inference: Best Practices
Integrating Model Inference in an AppIntegrating model inference in an app involves incorporating a trained machine learning model into the application to make predictions on new data. This can be achieved by using an API to connect the model to the app. The API can be hosted on a cloud platform or on-premises server. The app sends data to the API, which then returns the predicted output. Defining Model as a ServiceDefining the model as a service involves decoupling the model from the application. This means that the model is hosted separately from the application and can be accessed by multiple applications. This approach has several advantages, including:
Responsibilities for Model Ops and Dev OpsModel ops and dev ops are responsible for ensuring that the model is deployed and maintained correctly. Model ops is responsible for managing the model's lifecycle, including training, testing, and deployment. Dev ops is responsible for managing the application's lifecycle, including deployment, monitoring, and maintenance. When an application uses a model, both model ops and dev ops need to work together to ensure that the model is integrated correctly into the application. Other Options for Integrating the ModelAnother option for integrating the model is to include it as part of the application. This approach has some advantages, including:
However, this approach has some disadvantages, including:
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