TensorFlow Lite or Core ML Model Conversion & Mobile Integration

I will convert your existing trained machine learning model into a mobile-optimised format — TensorFlow Lite (.tflite) for Android or Core ML (.mlmodel) for iOS — and integrate it into your mobile app with a clean inference wrapper that handles model loading, input preprocessing, inference execution, and output post-processing. Running AI models on-device eliminates API latency, removes inference costs, and enables your app to function fully offline — critical advantages for real-time mobile AI features like image recognition, text classification, or gesture detection.

The conversion process includes model quantisation (INT8 or float16) to reduce model size and improve inference speed on mobile hardware, accuracy benchmarking before and after quantisation to validate performance, and a platform-specific integration module (Swift/Objective-C for iOS, Kotlin/Java for Android, or Dart for Flutter) that your development team can drop directly into the existing codebase.
This service suits mobile development teams who have a trained Python model (TensorFlow, PyTorch, or scikit-learn) and need it running efficiently on a mobile device without rebuilding the model from scratch in a mobile-native framework.