Machine Learning Applications in IoT

Back in 2017, I explored the burgeoning relationship between Machine Learning (ML) and the Internet of Things (IoT). Since then, both fields have matured significantly, moving beyond hype to deliver tangible value across multiple industries. Today, ML and IoT are not just buzzwords; they are critical components driving innovation, efficiency, and safety in sectors ranging from manufacturing to automotive to retail.

This is an update to my original 2017 post, incorporating modern examples and a reflection on the growing role of cameras as sensors.

Data Analytics vs. Machine Learning

There was a lot of hype around machine learning when I originally wrote this post, and with the advent of ChatGPT, the hype has increased exponentially. Just about every major company is asking themselves “what is our AI strategy?”. Today “AI” has become an umbrella term for a wide range of applications of machine learning, including LLMs like ChatGPT, but in this post I’ll be primarily focused on non-LLM applications of machine learning.

At a high level, machine learning takes large amounts of data and generates useful insights that help the organization. That could mean improving processes, cutting costs, creating a better experience for the customer, or opening new business models.

The thing is, most organizations can get many of these benefits from traditional data analytics, without the need for more complicated machine learning applications.

Traditional data analytics excels at explaining historical trends, helping organizations track goals and support decision-making. But when dealing with high-volume, unstructured, real-time data—like sensor streams from IoT systems—machine learning becomes essential. ML enables systems to adapt dynamically, uncover hidden patterns, and make predictions in ways that static models cannot.

When Does Machine Learning Shine?

The data models that are typical of traditional data analytics are often static and of limited use in addressing fast-changing and unstructured data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.

While traditional data analysis would need a model built on past data and expert opinion to establish a relationship between the variables, machine learning starts with the outcome variables (e.g. saving energy) and then automatically looks for predictor variables and their interactions.

In general, machine learning is most valuable when you know the outcome you want but don’t yet know which variables matter most. ML works by identifying those key predictors automatically, learning from vast amounts of data. So, you give the machine learning algorithm the goal and then it “learns” from the data which factors are important in achieving that goal.

A great example (though nearly a decade old as of this update) is Google’s application of machine learning to its data centers in 2016. Data centers need to remain cool, so they require vast amounts of energy for their cooling systems to function. This represents a significant cost to Google, so the goal was to increase efficiency with machine learning.

With 120 variables affecting the cooling system (i.e. fans, pumps speeds, windows, etc.), building a model with classic approaches would be a huge undertaking. Instead, Google applied machine learning and cut its overall energy consumption by an astounding 15%. That represents hundreds of millions of dollars in savings for Google in the years since.

The Pivotal Role of Cameras as Sensors

When I originally wrote this post in 2017, there were many emerging use cases for ML and IoT but it was unclear which would become widespread. Today, it’s clear that ML applied to cameras (aka computer vision, or vision AI) is among the most powerful and fastest growing applications of ML plus sensors.

In retrospect, it feels fairly obvious why cameras have emerged as one of the most powerful sensors:

• Cameras are commoditized: unlike many bespoke IoT devices, which are expensive and custom, cameras are relatively cheap and plentiful with a deep vendor ecosystem.
• Cameras are already deployed: many businesses already have cameras deployed for security purposes, so it’s possible to leverage existing infrastructure and thus minimize additional CapEx to deploy sensors.
• Cameras are highly flexible: when intelligence is applied to video feeds, dozens of value-adding use cases can be unlocked from the same sensor stream. Unlike other IoT sensors, where a different sensor is needed for each use case, the same cameras just need new ML models to be trained and deployed to create additional value.

Thanks to their versatility and ubiquity, cameras allow businesses to unlock dozens of high-value use cases—from monitoring compliance to tracking customer journeys—all from a single hardware source.

Real Applications of ML + IoT

Let’s take a look at a few different industries with some concrete examples of machine learning applied to sensor data

Automotive

The automotive industry has become a major adopter of ML-powered IoT solutions, applying them across both vehicle operations and manufacturing environments. From real-time performance analysis in service bays to automated quality checks on assembly lines, machine learning helps automakers improve reliability, reduce downtime, and enhance service experiences. With vehicles and factories increasingly instrumented with sensors—and many already equipped with connected camera systems—automotive companies are using ML to turn raw data into real operational value.

• Predictive Maintenance: Automotive manufacturers are leveraging machine learning to analyze real-time sensor data from vehicles and factory equipment, predicting component failures before they occur. This proactive approach reduces unplanned downtime and maintenance costs.
• Defect Detection: Automotive factories are integrating ML-driven computer vision systems to monitor assembly lines, ensuring quality control and reducing defects. These systems detect anomalies in real-time, allowing for immediate corrective actions.
• Service Bay Efficiency: Service centers are adopting AI-driven systems that utilize computer vision to monitor bay utilization, technician performance, and service times. These systems provide real-time insights, enabling managers to optimize workflows and improve efficiency.

See how Leverege PitCrew is equipping managers and technicians with automated insights into bay utilization, service times, and technician performance to achieve higher efficiency, lower wait times, and increased revenue in automotive service centers.

Manufacturing

Manufacturing has become one of the clearest beneficiaries of ML-powered IoT. Sensors embedded in production lines continuously collect data on temperature, vibration, pressure, speed—and increasingly, high-resolution visual data. This data, combined with machine learning, enables transformative gains in:

• Quality Inspections: ML-driven vision systems are replacing manual defect detection in factories. These systems are more accurate and consistent, catching issues like scratches, misalignments, or faulty welds before they reach customers.
• Process Optimization: Sensor data from industrial equipment is analyzed using ML to optimize production schedules, energy consumption, and machine settings.
• Worker Safety & Compliance: Factories are integrating ML with cameras and wearables to monitor safety compliance. These systems can detect missing protective gear, risky behaviors, or signs of fatigue.

See how Leverege WorkWatch is improving quality, compliance and productivity with actionable insights across production lines and manufacturing plants—keeping manufacturing workforces safe and operations efficient.

Retail

In brick-and-mortar retail, ML and IoT are driving operational efficiency and improving the customer experience. Cameras—already widely deployed for security—are now being used to power real-time insights that optimize stores and reduce costs:

• Loss Prevention & Shopper Safety: Retailers are utilizing ML-powered surveillance systems to detect and prevent theft, enhancing store security and reducing inventory shrinkage.
• Customer Journey Analytics: Machine learning analyzes data from various sources, such as foot traffic sensors and sales transactions, to understand customer behavior and optimize store layouts and product placements.
• Queue Analytics & Management: Retailers are implementing AI-powered queue optimization systems that provide real-time visibility into customer wait times, enabling staff to address bottlenecks promptly and improve service efficiency.

See how Leverege ExpressLane is reducing queue abandonment and increasing sales with actionable insights derived from real-time data that track every step of the customer journey—from entry to checkout.

We’re Just Getting Started

Machine learning and IoT are no longer emerging technologies—they’re proven tools driving real-world impact across industries. By combining intelligent algorithms with sensor-rich environments, organizations in automotive, manufacturing, and retail are improving efficiency, reducing costs, and delivering better experiences for customers and employees alike. As the technology continues to mature and become more accessible, the question is no longer if ML and IoT will transform operations, but how quickly businesses can adapt to stay ahead.

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