Your mobile app processes biometrics on the server — every request goes through the network, data accumulates in the cloud, and latency stretches to a second? This is a classic problem for medical, financial, and enterprise applications. On-device ML radically changes the approach: the model lives directly on the user's device, training and inference happen locally. We build such systems turnkey — from prototype to production. No data transmission, no leaks, p99 latency in single-digit milliseconds (typically 5–20 ms). In 4–8 weeks you get a working solution. Our certified engineers have 6+ years of experience in on-device ml and have completed 30+ projects for FinTech, MedTech, and enterprise clients, serving over 100,000 users across 50+ device types.
We implement on-device ML using Core ML for iOS and TFLite for Android, enabling private compute and edge AI. Our certified engineers specialize in federated learning and on-device inference, ensuring data privacy and low latency.
Why choose On-Device ML?
On-Device ML is the only way to comply with HIPAA, GDPR, and corporate security policies. It enables private compute and edge AI, using Core ML or TFLite for on-device inference and federated learning for locally trained models. This ensures data privacy and fast mobile ML. Modern mobile chips with NPU (Neural Engine, Google Tensor) deliver performance comparable to cloud. On-device inference is 10x faster than cloud at p99 latencies, and on-device training allows personalization per user without sending data. Cloud infrastructure cost reduction reaches 70%, saving $10,000–$50,000 annually for medium-scale apps. For example, a fintech client saved $35,000 per year by switching to on-device inference. Another client with 100,000 users reduced cloud costs from $5,000/month to $500/month, saving $54,000 annually.
How we implement on-device inference
Inference is a relatively straightforward task. The model is trained on the server, then deployed to the device with optimization for specific hardware.
| Platform | Framework | Hardware acceleration |
|---|---|---|
| iOS | Core ML | Neural Engine (ANE), GPU |
| Android | tflite | NNAPI, GPU, Hexagon DSP |
| Embedded | TFLite Micro, ONNX Runtime Mobile | ARM Neon, CMSIS-NN |
Typical case: face unlocking on a smartphone. We trained MobileFaceNet on the server, converted to Core ML with INT8 quantization (model size 2.3 MB, down from 10 MB), and integrated into the app. Inference takes 15–20 ms on modern iPhones, achieving 98% accuracy. For mobile ML, we leverage Core ML and TFLite for efficient on-device inference.
Comparison of on-device inference and training
| Characteristic | Inference | Training |
|---|---|---|
| Goal | Execute a pre-trained model | Fine-tune on local data |
| Memory consumption | Low (1–3x weights) | High (3–6x weights) |
| Power consumption | Moderate | High (only while charging) |
| Frequency | Continuous | Periodic (nighttime) |
| Implementation complexity | Medium | High (federated learning) |
How federated learning solves the privacy problem?
On-device training is more complex: backpropagation requires ~3x memory compared to inference, plus power consumption. We use federated learning: devices fine-tune the model on local data, send only gradient updates (not data), the server aggregates them via FedAvg. Stack: TensorFlow Federated, PySyft, FATE.
Example: a keyboard engine with personalized typing style. The device trains a small transformer on top of Federated EMNIST — only last layer, Adam with gradient clipping. The process runs during charging. Result: +15% accuracy without user data leakage, with additional power consumption not exceeding 5% of battery overnight.
What's included in your On-Device ML solution
- Project requirements analysis and feasibility study
- Model architecture selection and optimization (quantization, pruning, LoRA)
- Framework integration (core ml, tflite, ONNX Runtime)
- Testing on target devices (iPhone, Pixel, Samsung, etc.)
- Documentation (API docs, deployment guide, troubleshooting)
- Post-deployment support (3 months free)
- Training for your team (2 hands-on sessions)
- Access to model versioning system (MLflow)
Process (what is included)
- Analysis — determine whether on-device training or inference is needed, select architecture (MobileNet, TinyBERT, etc.), estimate memory budget and FLOPS. At this stage we provide a detailed technical report.
- Design — develop pipeline: server training → quantization/pruning → device deployment; for training — configure federated cycle. We use MLflow for model versioning.
- Implementation — integrate with core ml / tflite, support batching, async inference. Write unit tests and integration tests on target devices.
- Testing — measure p99 latency, power consumption, accuracy on real data. Use Xcode and Android Studio profilers for fine-tuning.
- Deployment — release model via CDN or within APK/IPA. Document error handling and graceful degradation (fallback to cloud inference when memory is low).
Timelines: 4–8 weeks depending on complexity and number of platforms. Cost is calculated individually — contact us for an evaluation of your case. Typical project cost ranges from $15,000 to $50,000. Get a consultation: our engineers will assess the project in 1 day. Reach out to discuss your project.
Common mistakes in On-Device ML implementation
- Ignoring battery: training without charging kills user experience. We always use
BatteryState.chargingtrigger. - Overly large model: context window > 512 tokens is almost unfeasible on mobile GPUs. We use LoRA and pruning.
- Lack of fallback: when memory is low, we switch to cloud inference. We implement graceful degradation.
We guarantee the performance and security of your solution. Contact us to discuss your project.
On-device ML is transforming industries by enabling private compute, edge ai, and data privacy. Whether you need on-device inference for real-time applications or on-device training with federated learning, we deliver locally trained models that respect user privacy. Our expertise in core ml, tflite, and federated learning ensures your mobile ml solutions are fast, efficient, and secure.