AIoT Ops: A step forward in your 4.0 industry strategy

PROFESSIONALIZE YOUR DEVICE ARMY WITH MDW AND MICROSOFT AZURE

Today, many companies rely on hundreds of AI models and thousands of IoT devices. These solutions help with sales forecasting, customer churn prediction, image recognition, and many other business needs.

Industries such as manufacturing, supply chain, and energy benefit even more by combining the power of AI and IoT. IoT devices are already present in factories and plants, and many of them can now run AI models directly on the edge. Thanks to this, companies can implement capabilities such as detecting defects, ensuring safety zones, identifying PPE on workers, counting people or pieces, and scanning delivery notes.

These solutions represent a major step forward in Industry 4.0 and bring significant improvements in revenue, safety, and automation.

Figure 1: Industrial revolutions. Now we are in the era of AI and IoT.

The Latest Requirements in Industry

AI and industry evolve together. In the past decade, the main question was whether AI could solve specific business problems. Today, we already have clear evidence that it can. The new priority is to build a sustainable, secure, and governed environment for AI and IoT at scale.
Important questions appear quickly:

Which AI model version runs on which IoT device?
How can we update models safely?
What do we do when a model’s performance drops?
How do we manage many devices with different architectures?

Imagine a Data Science team developing a computer vision model to detect defects in metallurgical pieces. They deploy the first model across multiple IoT devices. Later, a new team member develops a better version using another architecture. Newer devices arrive too, requiring different types of modules. After several months, the company may have dozens of models, versions, images, and deployments — without a centralised system.

This complexity makes it essential to create a centralised and controlled environment for Data Scientists.

Figure 2: Necessity of a centralized environment between Data Scientists

Three Components of an AIoT Solution

Building a professional AIoT ecosystem requires combining three areas: IoT, AI, and DevOps.

1. IoT

IoT devices are a cornerstone of Industry 4.0. They collect data, monitor equipment, warn operators of risks, and now execute AI models directly on the edge. Edge deployments bring important advantages:

Lower latency
No dependency on cloud connection
Real-time insight
Reduced bandwidth consumption

However, IoT devices present unique constraints:

Many have limited size, memory, and processing power.
Large AI models must be simplified through model quantization.
IoT devices use different architectures such as ARM32, ARM64, or GPU.

Because of these differences, companies often need to create one module per device type, packaged most commonly using Docker containers.

A Docker image used for IoT must include:

A trained AI model
A base image adapted to the device’s CPU/GPU architecture
A module that sends predictions to internal systems and the cloud

Companies often need multiple images for different devices, all stored in a registry such as Docker Hub or Azure Container Registry.

IoT Hub: From Modules to Massive Deployment

Azure IoT Hub offers:

Bi-directional communication
Secure device identity
Module deployment
Telemetry routing

It connects software (modules) and hardware (IoT devices), enabling organised deployments at scale.

Figure 3: Basic IoT architecture: Registry of modules, devices, and cloud service for bi-directional communication

2. AI and MLOps

Although AI models differ, AI projects follow similar phases:

Exploratory analysis
Feature engineering
Model training
Deployment
Monitoring

MLOps automates these steps and ensures they are repeatable, traceable, and governed.

Azure Machine Learning Services

Azure ML centralises:

Experiments
Metrics
Models
Docker images
Compute environments

This prevents Data Scientists from storing models on their personal computers and ensures full reproducibility.
Only a few lines of extra code are needed to register models, log experiments, and track every step.

3. DevOps

DevOps orchestrates the entire AI and IoT solution.
A DevOps pipeline allows teams to automate tasks such as:

Running feature engineering
Training AI models
Registering the best model
Building IoT modules
Pushing modules to different registries
Deploying modules to IoT devices

Unique triggers for AIoT pipelines include:

New training data
New model version
New model architecture
New IoT device generation

With DevOps, Data Scientists become more autonomous, and IT teams focus on higher-value tasks.

Once the model part is completed, the pipeline continues with the creation and deployment of IoT modules.

AIoT Ops: End-to-End Solution

By combining AI, IoT, DevOps, and Azure services, companies achieve a complete end-to-end AIoT solution:

Process data
Train models
Register models
Build Docker modules
Deploy modules to devices

This ensures full control over models, devices, versions, and deployments.

This final graphic summarises the entire journey from raw dataset to an intelligent IoT device in production.

Did You Know?

Building a DevOps pipeline for AI and IoT may seem complex, but the long-term benefits are significant. MLOps is already recognised for improving AI quality and reliability. Extending these practices to IoT deployments accelerates the transformation towards Industry 4.0.

After a decade of experimentation, companies now need governance, security, and productivity.
AIoT Ops is the next step.