Connected products generate constant streams of information about how they are operating, where they are deployed, and how users interact with them. That data creates an opportunity to move beyond simple monitoring and into systems that can recognize patterns, predict outcomes, and improve behavior over time. That is where machine learning becomes valuable.
For companies building connected products, machine learning is no longer an optional layer reserved for advanced analytics teams. It is becoming a practical way to enhance product capability, improve service operations, reduce downtime, and create more intelligent user experiences. IoT makes that possible by producing the telemetry, state data, and real-world feedback that machine learning depends on. AI then builds on that foundation by helping organizations turn those insights into decisions and automate actions at scale.
Connected devices do not create business value just by being online. They create value when the data they generate leads to faster decisions, drives action in real workflows, and improves measurable outcomes in the field.
Where machine learning has been and where it is today
Machine learning has been used in industrial and commercial systems for years, often in focused applications such as anomaly detection, predictive maintenance, or quality control. In many cases, these models lived in back-office systems and were trained on historical data pulled from equipment logs or service records.
What has changed is the quality, volume, and immediacy of connected product data.
Modern IoT systems can stream telemetry continuously or at defined intervals. Devices can report temperature, pressure, vibration, current draw, runtime, battery state, error codes, and many other signals. Products can also capture user interactions, event histories, and environmental conditions. That richer data foundation makes machine learning more useful because models can be trained on real operating conditions rather than small static datasets.
At the same time, model deployment has become more flexible. Some models still run centrally in the cloud, where compute is plentiful and updates are easier to manage. Others now run at the edge, directly on gateways or embedded systems. That shift has made machine learning part of the product itself rather than just part of a reporting system behind the scenes.
Telemetry is the starting point
Machine learning in IoT begins with telemetry. If the data coming off a connected product is incomplete, inconsistent, or poorly structured, model performance will suffer no matter how advanced the algorithm is.
Connected products generate several categories of useful data:
- Operational telemetry, such as sensor readings and runtime metrics
- Event data, such as alarms and fault conditions
- Context data, such as location or environmental conditions
- System health data, such as connectivity quality and resource usage
- Service and outcome data, such as maintenance history or confirmed failures
This mix matters because most IoT machine learning workflows depend on combining raw telemetry with context and known outcomes. A vibration signal alone may indicate many things. A vibration signal combined with motor age, operating temperature, duty cycle, and maintenance events can support a much stronger failure prediction model.
For connected product teams, telemetry design is not just a firmware or cloud architecture concern. It is also a machine learning concern. The data model must support training, validation, deployment, and long-term improvement. It also needs to support how model outputs will be used in practice, whether that is triggering service workflows, informing operators, or driving automated responses.
How models are trained and what types of models are used
Once telemetry is collected, the next step is turning that data into model inputs. This involves cleaning, normalizing, and aligning data. Timestamps must match, missing values must be handled, and noise may need filtering. Features are often derived from raw streams, such as rolling averages or rates of change.
Training then depends on the problem being solved.
For many IoT applications, common model types include:
- Classification models that determine whether a condition belongs to a known category
- Regression models that estimate numeric outcomes such as remaining useful life
- Anomaly detection models that identify behavior outside expected patterns
- Time series models that forecast future values
In equipment monitoring, a classification model might determine whether a compressor is operating normally or showing signs of imbalance. A regression model might estimate how many hours remain before a component reaches end of life. An anomaly detection model might flag a new pattern that has not appeared before.
Training usually happens in the cloud or in a centralized data environment because it requires access to larger datasets and more compute resources. The result is a trained model artifact along with the logic needed to process inputs, generate predictions, and connect those predictions to downstream actions.
Deployment is where architecture decisions matter
Once a model is trained, product teams must decide where inference should happen.
Edge deployment
Running a model at the edge means inference happens on or near the device. This is valuable when low latency matters, connectivity is intermittent, or bandwidth needs to be controlled.
Edge deployment is often the right choice when:
- A device must respond immediately to changing conditions
- Sending all raw data to the cloud is impractical
- Operations must continue even when connectivity is lost
For example, a gateway monitoring rotating equipment may run a local anomaly detection model that identifies abnormal vibration within seconds and triggers an alert before the cloud is involved.
The tradeoff is that edge environments have tighter constraints. Compute, memory, and power are limited, and model updates can be more complex.
Cloud deployment
Running inference in the cloud supports larger models and centralized management. It also allows model logic to be updated once and applied across an entire fleet.
Cloud deployment is often the right choice when:
- The model requires heavier compute
- Predictions depend on fleet-wide context
- Data from many devices improves accuracy
For example, failure prediction across a fleet of industrial assets may benefit from cross-site comparisons and historical trends that are not available locally.
The tradeoff is latency and dependency on connectivity.
In many real-world systems, the best approach is hybrid. Lightweight models or rules run at the edge for immediate action, while the cloud performs deeper analysis and fleet-level optimization. In practice, this often means immediate, localized decisions happen at the edge, while the cloud supports more complex reasoning and coordination across systems and workflows.
How IoT empowers AI and machine learning
IoT is what makes machine learning operational in the physical world.
Without connected products, many AI and ML systems remain disconnected from real usage and real outcomes. IoT creates a loop between device behavior, telemetry, analytics, and action.
A practical workflow looks like this:
- A connected product generates telemetry
- Data is transmitted to a platform or pipeline
- The data is cleaned and stored
- Models are trained using historical data
- Models are deployed to the cloud, the edge, or both
- Live data is scored in production
- Predictions trigger actions such as alerts, service recommendations, parameter changes, or integration into existing workflows and systems
- Results are captured and fed back into the system
Machine learning is only useful when outputs can be tied to outcomes and when those outcomes can be fed back into the system to improve future decisions. In more mature systems, this step moves beyond alerts into structured decision-making, where model outputs consistently drive the next best action within service, operations, or commercial workflows.
Real-world applications
A common example is equipment monitoring. A connected pump, motor, or HVAC asset can stream temperature, vibration, current, and runtime data. Machine learning models use those inputs to identify patterns associated with wear or impending failure.
The edge system may detect immediate anomalies, while the cloud correlates long-term trends across the fleet. The result is earlier intervention, fewer unplanned outages, and more consistent service decisions across the fleet.
Another example is usage optimization. A connected product can learn how it is used in the field and adjust settings or maintenance timing based on observed behavior. In this case, machine learning enhances product capability directly by influencing how the product operates, rather than simply producing an insight for someone to interpret.
Conclusion
AI, machine learning, and IoT work together because each solves a different part of the problem. IoT creates visibility through connected devices and telemetry. Machine learning turns that data into predictions and context. AI helps organizations scale decision-making and consistently drive action across products, fleets, and workflows.
For connected product teams, the opportunity is practical. Better telemetry design leads to better data. Better data supports stronger models. Better deployment decisions determine whether those models deliver value in the field. When all of that is connected through a feedback loop, products become more capable, more responsive, and more valuable over time.
Many organizations already have the data and the models in place. The gap is turning those outputs into consistent, repeatable actions that operate at scale. That is where most connected product initiatives stall, and where the next phase of value is created.
If you’re looking to move from connected product data to consistent, automated action, explore how leading manufacturers are closing that gap at scale with our latest ebook, “The Last Mile of Connected Product ROI.”