Mobile AI apps require storing machine learning models and related metadata locally to function offline. Mobile databases like Realm or SQLite can store serialized model files, configuration parameters, or training data summaries. This local storage enables apps to load and run models quickly without cloud dependency.
Additionally, apps may store user feedback or inference logs locally mobile database to improve model accuracy via on-device retraining. Proper versioning and schema management are important to update models without corrupting stored data. Storing ML models in mobile databases ensures efficient access, faster load times, and better privacy, as sensitive inference data stays on-device.
Mobile Databases in Augmented Reality (AR) Apps
AR applications blend digital content with the real world and require rapid access to location, object recognition data, and user preferences. Mobile databases store spatial maps, 3D model metadata, and interaction logs locally to enable smooth AR experiences without lag. Realm or SQLite databases manage this data, allowing quick retrieval and updates as users move through physical environments. Offline storage is crucial because AR apps often operate outdoors or in variable network conditions. Efficient indexing and compact data formats reduce latency and power consumption. Integration with device sensors and databases creates immersive, real-time augmented reality applications.