How to deploy XGboost trained model in android?
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Deploying an XGBoost model directly to Android poses challenges due to its complexity. However, there are alternative approaches to achieve your goal:
Leverage TensorFlow Lite: - Convert your XGBoost model to a TensorFlow Lite format using tools like XGBoost4J or custom conversion scripts. Integrate the converted model into your Android app using the TensorFlow Lite Support Library. This approach offers a well-documented and supported path for on-device inference.
Utilize cloud-based inference:- Host your XGBoost model on a cloud platform like Google Cloud AI Platform or Amazon SageMaker. Develop your Android app to communicate with the cloud service and send data for predictions. This method keeps the model off the device, potentially saving resources and simplifying deployment.
Explore third-party libraries: - Libraries like ML Kit or Scopelo offer pre-trained models and functionalities for various tasks on Android, potentially eliminating the need for custom model deployment.
Let us know if any of the above idea works for you.