What are dependency injection approaches for Core ML in Swift?

Dependency injection is a design pattern that allows for better management of dependencies in your applications, making them more testable and maintainable. In the context of Core ML in Swift, several approaches can be utilized to implement dependency injection. Here are some popular methods:

Constructor Injection: Pass the Core ML model to the class that needs it via the initializer.
Property Injection: Set the Core ML model as a property after the object has been initialized.
Service Locator: Use a service locator pattern to fetch the Core ML model when needed.
Factory Pattern: Create a factory that can produce instances of your Core ML model with the appropriate configurations.

Here’s a brief example of constructor injection with a Core ML model:


        class ModelHandler {
            private let model: MyCoreMLModel
            
            init(model: MyCoreMLModel) {
                self.model = model
            }
            
            func predict(input: ModelInput) -> ModelOutput? {
                // Perform predictions using the model
                do {
                    let output = try model.prediction(input: input)
                    return output
                } catch {
                    print("Error making prediction: \(error)")
                    return nil
                }
            }
        }
        
        // Usage
        let myModel = MyCoreMLModel()
        let modelHandler = ModelHandler(model: myModel)
        let result = modelHandler.predict(input: inputData)

What are dependency injection approaches for Core ML in Swift?

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What are dependency injection approaches for Core ML in Swift?

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