What are error handling patterns for Core ML in Swift?

When working with Core ML in Swift, it's essential to implement effective error handling to ensure robust applications. This involves anticipating potential errors during model loading, predictions, and data processing, and responding appropriately to maintain a smooth user experience.

1. Handling Model Loading Errors

It's crucial to handle errors when loading your Core ML model. This can be done using a do-catch block:


        do {
            let model = try YourModel(configuration: MLModelConfiguration())
            // Use the model for predictions
        } catch let error {
            print("Error loading model: \(error.localizedDescription)")
            // Handle the error accordingly
        }

2. Handling Prediction Errors

When making predictions, you may encounter various errors based on input data or model specifications. It's advisable to validate the input before passing it to the model:


        do {
            let prediction = try model.prediction(input: inputData)
            // Use the prediction result
        } catch let error {
            print("Error making prediction: \(error.localizedDescription)")
            // Handle the error
        }

3. Data Validation

Make sure to validate the data format and type before passing it to the Core ML model. This can prevent runtime errors and improve reliability:


        guard let validInput = validateInput(inputData) else {
            print("Invalid input data.")
            return
        }
        // Continue with prediction

Core ML Swift Error Handling Model Loading Predictions Data Validation

What are error handling patterns for Core ML in Swift?

1. Handling Model Loading Errors

2. Handling Prediction Errors

3. Data Validation

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What are error handling patterns for Core ML in Swift?

1. Handling Model Loading Errors

2. Handling Prediction Errors

3. Data Validation

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Popular Topics

Recent Languages