Mobile AI revolution is increasingly moving on-device, driven by demands for privacy, low latency, and offline capability. In this session, I’ll demonstrate how to leverage cutting-edge on-device AI tools including Gemini Nano, Prompt API, ML Kit Gen AI API and MediaPipe LLM Inference APIs to build intelligent Android apps entirely on-device.

Key Takeaways:

* On-Device AI Landscape - Understand the shift from cloud to on-device AI, the privacy benefits, and real-world use cases for features like smart reply, summarisation, image analysis, and breaks down how Android abstracts on-device intelligence through AI Core, LoRA fine-tuning, Private Compute, and hardware accelerators like NPUs..

* Getting Started with Gemini Nano - Walk through integrating Google’s Gemini Nano generative model into a modern Android app, highlighting both ML Kit Gen-AI APIs and Prompt API for custom scenarios.

* Prompt API demo - Through real-world demo of AI-driven activity booking search system built on Gemini Nano using prompt api (optimising using prompt design strategies and best practice from 18s -> 3s ), shows how to design, optimize, and productionise on-device AI features for modern Android apps.

* Production Considerations - Address model size, device compatibility, privacy, and performance optimisation lessons learned from deploying AI features at scale in consumer and enterprise Android apps.

* Beyond Gemini - MediaPipe, LiteRT, and Custom Models: Explore the MediaPipe ecosystem for LLM (large language model) inference on-device, and how to bring your own models using LiteRT/TensorFlow Lite for specialised tasks.

Session presented on Droidcon Uganda 2025 on 10th November.