Portfolio write-up of my work on Project Echo, a real-time Australian wildlife-monitoring platform built at DataBytes. IoT Raspberry Pi sensors capture audio in the field, on-device ML classifies the species, and detections flow via MQTT to a FastAPI backend and a live-map dashboard — deployed as a 10-service stack on GCP/GKE.
The platform code lives in the DataBytes org repo. This repo documents my specific contributions and links to the real pull requests, rather than republishing org code.
Role: ML Engine Co-Lead (T3 2025) → Project Lead (T1 2026) Stack: Python · PyTorch · TensorFlow/TFLite · ONNX · MQTT · Docker · Kubernetes (GKE) · MLflow · FastAPI
Designed and shipped an IoT edge-inference pipeline running EfficientNetV2 (TFLite) directly on Raspberry Pi nodes. A dual-mode MQTT design sends structured detections (~300 bytes of JSON) instead of raw audio (~500 KB) — a >99% cut in per-detection bandwidth — while preserving full backward compatibility.
Diagnosed and fixed a hostname-resolution bug that broke both local Docker Compose and
GKE cloud deploys. Root cause: API_HOST not propagating correctly between Kubernetes
service names and Compose hostnames.
Built the quantisation pipeline achieving 69–71% model size reduction while classifying
100+ wildlife species in real time. Verified EfficientNet operator compatibility for the
standard TFLite runtime (no custom ops), and built a multi-backend inference engine
(ModelLoader + abstract InferenceEngine) switching dynamically between ONNX Runtime and
the TFLite Interpreter.
Built the engine-side MQTT integration with 14 unit tests covering payload validation, broker configuration, and race-condition handling.
PyTorch training framework (Hydra config) supporting EfficientNetV2 and PANNs models, ArcFace / Circle loss, mixed-precision (bfloat16) training, SpecAugment, and LMDB caching — with all experiments tracked in MLflow.
IoT Sensors → MQTT Broker → ML Engine → TensorFlow Serving → FastAPI → MongoDB/Redis → HMI
- Platform (org repo): https://github.com/DataBytes-Organisation/Project-Echo
- LinkedIn: https://www.linkedin.com/in/raul-movila