Edge AI Deployment Built for the Field

Under Services → TinyML & Edge AI → Edge AI Deployment, WorkSprout ships inference where data is born — OTA update paths, MQTT and gRPC gateways, fleet dashboards, secure provisioning, and rollback playbooks for production edge nodes.

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.

GridFlow

Edge AI Deployment

Q4 2025

WorkSprout Edge Ops Team

GridFlow: OTA Edge AI Fleet Across 500+ Nodes with Zero-Touch Model Rollouts

WorkSprout deployed a production edge AI fleet for GridFlow — staged OTA model and firmware updates, MQTT gateway orchestration, and Grafana monitoring across 500+ field nodes with 99.9% fleet uptime and sub-second telemetry.

MQTT Grafana Docker Terraform OTA GitHub Actions

12 Wk.Audit to Fleet

500+Nodes Managed

99.9%Fleet Uptime

100%Client Satisfaction

Edge Deployment Capabilities

What WorkSprout delivers for edge AI deployment — OTA rollouts, gateway bridges, fleet monitoring, and secure multi-site production cutover.

500+ Edge nodes managed

OTA model & firmware updates

Staged rollouts with canary groups and automatic rollback.

Edge gateway orchestration

MQTT, gRPC, and REST bridges between devices and cloud control planes.

Fleet monitoring dashboards

Device health, model version, and drift alerts in Grafana or Datadog.

Secure provisioning

Key rotation, signed artifacts, and tenant isolation at the edge.

Multi-site rollout playbooks

Repeatable deployment across factories, stores, or field sites.

Disaster recovery at the edge

Offline-first behaviour when cloud links drop for hours or days.

Explore TinyML services

What You Get with Edge AI Deployment

Fleet-ready deployment artefacts and operational playbooks — not lab demos — so edge inference survives real networks, power cuts, and scale.

OTA model registry

Signed bundles, version tracking, canary groups, and rollback on failure.

Gateway configuration kit

MQTT, gRPC, and REST bridge templates for your control plane.

Fleet health dashboards

Per-node model version, latency, and connectivity in Grafana.

Secure device provisioning

Key rotation, tenant isolation, and signed artifact verification.

Multi-site rollout scripts

Repeatable playbooks for factories, stores, and remote sites.

Operations runbooks

Incident response, rollback, and offline recovery procedures.

01 — Problem

Why Edge Deployments Fail

GridFlow had working edge models but no production path — manual USB updates, no fleet visibility, deployments that broke under real network conditions, and security reviews blocked by missing OTA design.

"We could not tell which model version ran on which node — every update meant a truck roll, and our lab deployment fell apart the moment connectivity dropped."

Manual USB updates required for every device in the field
No visibility into which model version is running on which node
Deployments that work in the lab but fail under real network conditions
Security reviews blocking launch because OTA paths were never designed in
No rollback path when a promoted model fails in production

Fleet Scale

500+ edge nodes across factories and remote monitoring sites.

Connectivity

Intermittent cellular and factory Wi-Fi with offline-first requirements.

Timeline

12-week deployment: 3 weeks audit, 5 weeks OTA/gateway, 4 weeks fleet pilot.

Deliverables

OTA registry, gateway configs, dashboards, rollout playbooks, and ops runbooks.

02 — Strategy

Our Deployment Approach

Design OTA and telemetry first, validate on representative networks, then roll out in canary groups with automatic rollback and offline-first fallbacks.

Fleet audit

Map nodes, networks, model versions, and operational SLAs.

OTA & gateway design

Signed update paths, MQTT bridges, and offline-first fallbacks.

Staged rollout

Canary groups, health gates, and automatic rollback on failure.

03 — Stack

Fleet & OTA Toolkit

Platforms we use to provision, update, monitor, and recover edge AI fleets in production.

On-Device ML Runtimes

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

Tensorflow

Pytorch

ARM

Arduino

Jupyter

Tensorflow

Embedded RTOS & MCU

FreeRTOS, Zephyr, STM32, and ESP32 firmware integration with deterministic inference scheduling. Applied to Edge AI Deployment engagements.

ESP32

ARM

Python

ARM

Linux

STM32

Edge Compute & Vision

Jetson, Coral, OpenCV, and GPU-class pipelines for perception workloads at the edge. Applied to Edge AI Deployment engagements.

ONNX

Raspberry Pi

MQTT

NVIDIA

OpenCV

Docker

Fleet OTA & Observability

MQTT telemetry, Grafana dashboards, Prometheus metrics, and CI/CD for model promotion. Applied to Edge AI Deployment engagements.

MQTT

Grafana

Prometheus

GitHub Actions

Docker

Python

04 — Process

Deployment Delivery Process

Fleet audit → OTA design → gateway integration → staged rollout → monitoring — with health gates before every production promotion.

Discover

Fleet inventory, network constraints, and success metrics.

Design OTA

Update paths, signing, canary groups, and rollback rules.

Wire gateways

MQTT, gRPC, and REST bridges to your control plane.

Pilot rollout

Staged deployment with health monitoring on representative sites.

Fleet cutover

Production promotion with ops training and runbooks.

Care

Ongoing fleet monitoring, incident response, and optional retainer.

Tools Used: GrafanaMQTTDockerTerraformGitHub Actions

05 — Milestones

Deployment Snapshots

Visual milestones across a typical edge AI deployment engagement — from fleet audit through live production monitoring.

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06 — Delivery

Deployment Deliverables

OTA registry, gateway configs, fleet dashboards, and multi-site rollout playbooks delivered for GridFlow production nodes.

OTA pipeline, fleet dashboard, and rollout playbook in production

07 — In Field

Fleet Live in Production

How GridFlow edge nodes run in factories and remote sites — model versions, health telemetry, and OTA status across the full fleet.

Factory sites Fleet dashboard OTA promotions

worksprout.us/portfolio

Live

GridFlow Edge Fleet

500+ nodes · OTA model + firmware · MQTT gateways · Grafana · 99.9% uptime

View portfolio

DeliveredQ4 2025

Duration12 Weeks

ServiceEdge Deployment

Fleet nodes500+

Uptime99.9%

Satisfaction100%

08 — Impact

Results from Deployment Work

Within 90 days of fleet cutover, GridFlow eliminated manual updates, achieved 99.9% uptime, and rolled new models to every node without truck rolls.

500+ Nodes Managed

Edge AI fleet under unified OTA and monitoring.

<1s Telemetry Latency

Sub-second health and version reporting per node.

99.9% Fleet Uptime

Production availability across factories and remote sites.

Key outcome: Staged OTA with canary groups and automatic rollback meant model promotions shipped fleet-wide in hours — with full visibility into version and health on every node.

09 — Docs

Fleet & Operations Visuals

Architecture diagrams, OTA flows, and operations artefacts from the GridFlow deployment.

10 — Client Voice

Client Testimonial

★★★★★

"WorkSprout gave us a real deployment platform — OTA for models and firmware, MQTT gateways that survive flaky networks, and dashboards that show exactly what is running where. We went from USB truck rolls to zero-touch fleet updates in one programme."

11 — Workflow