TinyML Solutions That Ship Across Your Fleet

Under Services → TinyML & Edge AI → TinyML Solutions, WorkSprout runs full edge ML programmes — sensor data capture on device, training and evaluation, Edge Impulse and TFLM integration, and fleet-wide model rollout with OTA and telemetry.

Lightweight ML for Embedded Systems TinyML Solutions AI Engine Integration Edge AI Deployment Edge Model Optimization

Lightweight ML for Embedded Systems

Model selection, quantization, and deployment pipelines for microcontrollers and embedded targets — accurate inference within tight memory and power budgets.

TinyML Solutions

End-to-end TinyML programmes — sensor fusion, on-device training workflows, and production firmware integration for real-world edge products.

AI Engine Integration

Integrate TensorFlow Lite, ONNX Runtime, and vendor NPUs into existing firmware and application layers with stable APIs and observability.

Edge AI Deployment

Field deployment of edge AI — OTA update paths, device fleets, monitoring, and rollback strategies for production edge inference.

Edge Model Optimization

Pruning, distillation, INT8 quantization, and kernel tuning so models meet latency and energy targets on target silicon.

PulseGuard

TinyML Solutions

Q3 – Q4 2025

WorkSprout Edge ML Team

PulseGuard: Acoustic TinyML Across 200+ Edge Nodes for Predictive Equipment Monitoring

WorkSprout built an end-to-end TinyML programme for PulseGuard — on-device audio and vibration capture, Edge Impulse training pipelines, and OTA model delivery to 200+ field nodes monitoring industrial equipment with 97% field accuracy.

Edge Impulse TensorFlow Lite TFLM MQTT Grafana Python

14 Wk.Capture to Fleet

200+Nodes Deployed

97%Field Accuracy

100%Client Satisfaction

TinyML Programme Capabilities

What WorkSprout delivers for TinyML Solutions engagements — from device-side data capture through training, validation, and fleet model management.

50+ Edge devices in production

End-to-end TinyML programmes

From dataset capture on device through training, evaluation, and fleet deployment.

On-device data capture

Representative sensor streams collected in the field — not lab-only datasets.

Edge Impulse & TFLM pipelines

Production training and export workflows using industry-standard TinyML tooling.

Custom operator support

Novel layers and preprocessing when off-the-shelf graphs are not enough.

Fleet model management

Versioned models pushed to thousands of edge nodes with health telemetry.

Domain sensor stacks

Audio, vibration, vision, and environmental sensing on constrained hardware.

Explore TinyML services

What You Get with TinyML Solutions

Repeatable TinyML pipelines and fleet-ready artefacts — not one-off demos — so every device in your programme runs the same validated model with versioned OTA updates.

Edge Impulse project kit

Labelled datasets, impulse blocks, and export configs your team can extend.

Multi-target export bundles

TFLite, TFLM, and vendor formats from one validated training pipeline.

Fleet OTA model registry

Signed model versions, canary groups, and automatic rollback on failure.

On-device training hooks

Federated and incremental learning where connectivity is intermittent.

Field accuracy monitoring

Confidence, drift, and misclassification alerts per node and per model version.

Programme runbooks

Data capture, training, promotion, and fleet ops guides for your team.

01 — Problem

Why TinyML Programmes Stall

PulseGuard proved acoustic anomaly detection in the lab, but could not replicate results across hundreds of edge nodes — mismatched field data, no fleet pipeline, and vendor lock-in on a single dev board.

"We had a working TinyML demo — but no way to capture real sensor data at scale, train consistently, or push models to every node in the field."

Proof-of-concept models that could not be replicated across the device fleet
No pipeline from lab bench to manufacturing line deployment
Training data that did not match real field sensor conditions
Vendor lock-in on a single board with no portability plan
Manual USB flashes required for every model update in the field

Sensor Domains

Acoustic and vibration monitoring on industrial equipment nodes.

Fleet Scale

200+ edge nodes across factories and remote monitoring sites.

Timeline

14-week programme: 4 weeks data capture, 6 weeks train/export, 4 weeks fleet pilot.

Deliverables

Edge Impulse projects, OTA registry, monitoring dashboards, and ops runbooks.

02 — Strategy

Our TinyML Programme Approach

Device-first data collection, representative training sets, Edge Impulse and TFLM export paths, then staged fleet rollout with model versioning and health telemetry.

Sensor & fleet audit

Map devices, sensor types, connectivity, and accuracy SLAs across the fleet.

Field data programme

On-device capture workflows that build representative training datasets.

Train, export, validate

Edge Impulse pipelines with hardware validation before fleet promotion.

03 — Stack

TinyML Toolkit

Platforms and runtimes we use to build, train, export, and operate TinyML programmes across audio, vibration, vision, and environmental sensing.

On-Device ML Runtimes

TensorFlow Lite Micro, ONNX Runtime, CMSIS-NN, and PyTorch export paths sized for MCU flash and SRAM. Applied to TinyML Solutions engagements.

ONNX

Tensorflow

Pytorch

ARM

Arduino

Jupyter

Embedded RTOS & MCU

FreeRTOS, Zephyr, STM32, and ESP32 firmware integration with deterministic inference scheduling. Applied to TinyML Solutions engagements.

Python

ARM

Linux

STM32

ARM

Arduino

Edge Compute & Vision

Jetson, Coral, OpenCV, and GPU-class pipelines for perception workloads at the edge. Applied to TinyML Solutions engagements.

Tensorflow

ONNX

Raspberry Pi

MQTT

NVIDIA

OpenCV

Fleet OTA & Observability

MQTT telemetry, Grafana dashboards, Prometheus metrics, and CI/CD for model promotion. Applied to TinyML Solutions engagements.

InfluxDB

MQTT

Grafana

Prometheus

GitHub Actions

Docker

04 — Process

TinyML Delivery Process

Six stages from sensor audit through fleet care — with accuracy and latency gates on real hardware before every production promotion.

Discover

Sensor inventory, fleet topology, and success metrics documented.

Capture

On-device data collection in representative field conditions.

Train & export

Edge Impulse training, evaluation, and multi-target export.

Validate

Accuracy and latency gates on real hardware before promotion.

Fleet deploy

Staged OTA rollout with canary groups and rollback paths.

Care

Drift monitoring, retraining triggers, and optional MLOps retainer.

Tools Used: Edge ImpulseTFLite MicroGrafanaMQTTGitHub Actions

05 — Milestones

Programme Snapshots

Visual milestones across a typical TinyML Solutions engagement — from field data capture to fleet-wide model deployment.

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06 — Delivery

TinyML Deliverables

Edge Impulse projects, exported model bundles, fleet OTA registry, and monitoring dashboards delivered for PulseGuard production nodes.

Edge Impulse pipeline, fleet OTA kit, and monitoring stack in production

07 — In Field

TinyML Fleet In Production

How PulseGuard edge nodes run acoustic and vibration TinyML in factories and remote sites — model versions, drift alerts, and OTA status across the fleet.

Factory monitoring Fleet dashboard OTA promotions

worksprout.us/portfolio

Live

PulseGuard Edge Fleet

Edge Impulse · 200+ nodes · Audio + vibration · OTA model registry · 97% field accuracy

View portfolio

DeliveredQ4 2025

Duration14 Weeks

ServiceTinyML Solutions

Fleet nodes200+

Field accuracy97%

Satisfaction100%

08 — Impact

Results from TinyML Programme Work

Within 90 days of fleet cutover, PulseGuard achieved consistent field accuracy, eliminated manual model updates, and scaled monitoring to every deployed node.

200+ Fleet Nodes Live

TinyML models running on every PulseGuard edge node in production.

97% Field Accuracy

Acoustic anomaly detection matched lab benchmarks in real conditions.

OTA Zero-Touch Updates

Model promotions pushed fleet-wide without manual device flashes.

Key outcome: A single TinyML programme pipeline meant new sensor profiles and model versions rolled to the full fleet via OTA — no truck rolls, no per-device manual flashes.

09 — Docs

Pipeline & Fleet Visuals

Architecture diagrams, training reports, and fleet operations artefacts from the PulseGuard TinyML programme.

10 — Client Voice

Client Testimonial

★★★★★

"WorkSprout turned our TinyML proof-of-concept into a real programme — device data capture, Edge Impulse training, and OTA to 200+ nodes. Our field accuracy finally matched the lab, and we can promote new models without stopping production."

11 — Workflow